{"id":426,"date":"2013-08-08T23:42:05","date_gmt":"2013-08-09T04:42:05","guid":{"rendered":"http:\/\/hachmannlab.cbe.buffalo.edu\/?page_id=426"},"modified":"2022-05-11T08:58:34","modified_gmt":"2022-05-11T13:58:34","slug":"presentations","status":"publish","type":"page","link":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/research\/presentations\/","title":{"rendered":"Presentations"},"content":{"rendered":"

Research Presentations<\/h3>\n

Here is a listing of all the presentations, talks, and seminars (other than group meetings) given by members of the group:<\/p>\n

    \n
  1. J. Hachmann, A Machine Learning Shortcut to Physics-Based Modeling and Simulations<\/i>, Department Seminar, Department of Chemical Engineering, University of Arkansas, Fayetteville (AR), TBD 2022<\/b>. (invited talk)<\/li>\n
  2. J. Hachmann, TBD<\/i>, Theory and Applications of Computational Chemistry 2020, Sapporo (Japan), TBD 2022<\/b>. (invited talk)<\/li>\n
  3. J. Hachmann, TBD<\/i>, Workshop on Application of Machine Learning Algorithms to the Synthesis of Zeolites, Houston (TX), TBD 2022<\/b>. (invited talk)<\/li>\n
  4. J. Hachmann, TBD<\/i>, Physical Chemistry Seminar, University of Toronto, Toronto (ON), TBD 2022<\/b>. (invited talk)<\/li>\n
  5. J. Hachmann, A Machine Learning Shortcut to Physics-Based Modeling and Simulations<\/i>, Department Seminar, Department of Chemical and Biological Engineering, University of Wisconsin, Madison (WI), TBD 2022<\/b>. (invited talk)<\/li>\n
  6. J. Hachmann, The Machine Learning Route to Accelerated Discovery and Inverse Design of Materials Systems<\/i>, 2022 TechConnect World Innovation Conference, Symposium on AI for Advanced Materials Design, National Harbor (MD), June 2022<\/b>. (invited talk)<\/li>\n
  7. J. Hachmann, From Physics-Based to Data-Derived Models \u2013 and Back<\/i>, Center for Nonlinear Studies Annual Conference 2022 – Physics Informed Machine Learning, Santa Fe (NM), May 2022<\/b>. (invited talk)<\/li>\n
  8. A. Sonpal, Developing eXplainable Artificial Intelligence (XAI) Methods for the Molecular Sciences<\/i>, 2022 UB CBE Graduate Student Seminar, Buffalo (NY), May 2022<\/b>. (invited talk)<\/li>\n
  9. G. Vishwakarma, Adapting Evolutionary Algorithms for Data-driven Discovery of Liquid Organic Hydrogen Carriers<\/i>, 263rd ACS National Meeting, CINF Division Symposium on Chemical Information Across the Chemistry Enterprise, San Diego (CA), Mar 2022<\/b>. (poster)<\/li>\n
  10. G. Vishwakarma, Evaluating Homogeneous Catalysts for Hydrogen Extraction from Liquid Organic Hydrogen Carriers<\/i>, 263rd ACS National Meeting, ENFL Division Symposium on Energy Storage in Chemical Bonds: From Theory to Practice, San Diego (CA), Mar 2022<\/b>. (talk)<\/li>\n
  11. J. Hachmann, Making Machine Learning Work in Chemical and Materials Research<\/i>, Department Seminar, Department of Materials Science and Engineering, Pennsylvania State University, virtual, Dec 2021<\/b>. (invited talk)<\/li>\n
  12. G. Vishwakarma, Acceptorless Catalytic Dehydrogenation of Liquid Organic Hydrogen Carriers<\/i>, 24th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2021<\/b>. (poster)<\/li>\n
  13. A. Sonpal, Benchmarking Methods to Explain the Predictions of Black-Box Deep Neural Networks<\/i>, 24th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2021<\/b>. (poster)<\/li>\n
  14. J. Hachmann, The Machine Learning Route to Accelerated Discovery and Inverse Design of Chemical and Materials Systems<\/i>, 2021 AIChE Annual Meeting, CoMSEF Symposium on Data-Driven Design and Modeling, Boston (MA), Nov 2021<\/b>. (invited talk)<\/li>\n
  15. J. Hachmann, Tailoring Machine Learning for the Chemistry Domain<\/i>, Telluride Workshop on Machine Learning and Informatics for Chemistry and Materials, Telluride (CO), Sep 2021<\/b>. (invited talk)<\/li>\n
  16. N.M. Murthy, Synthetic Accessibility Scoring and its Potential Applications in Chemical Library Generation<\/i>, University at Buffalo MSc Thesis Defense Seminar, virtual, Aug 2021<\/b>. (talk)<\/li>\n
  17. D.N. Chheda, Machine Learning Classifiers for the Performance Prediction of High Refractive Index Polyimides<\/i>, University at Buffalo MSc Thesis Defense Seminar, virtual, May 2021<\/b>. (talk)<\/li>\n
  18. Y. Pal, Improved Energy Conversion Materials from Combined Data-Driven and Computational Screenings<\/i>, University at Buffalo PhD Dissertation Defense Seminar, virtual, May 2021<\/b>. (talk)<\/li>\n
  19. J. Hachmann, Advancing Computational Chemistry with Machine Learning: From Physics-Based to Data-Derived Models and Back<\/i>, Physical and Theoretical Chemistry Seminar, University of Graz (Austria), virtual, May 2021<\/b>. (invited talk)<\/li>\n
  20. Y. Pal, Materials Discovery Towards Cheaper Fuel Cell Vehicles<\/i>, 23rd Annual UB CBE Graduate Student Research Symposium, virtual, Nov 2020<\/b>. (poster)<\/li>\n
  21. G. Vishwakarma, A Virtual High-throughput Study for Traversing Energy Barriers in Regenerative Energy Systems<\/i>, 23rd Annual UB CBE Graduate Student Research Symposium, virtual, Nov 2020<\/b>. (poster)<\/li>\n
  22. A. Sonpal, Developing XAI Methodology for the Chemical Sciences<\/i>, 23rd Annual UB CBE Graduate Student Research Symposium, virtual, Nov 2020<\/b>. (poster)<\/li>\n
  23. A. Pradhan, Tailor-Made Materials – Inverse Engineering Compounds using Feature Correlation<\/i>, 23rd Annual UB CBE Graduate Student Research Symposium, virtual, Nov 2020<\/b>. (poster)<\/li>\n
  24. J. Hachmann, Advancing Computational Chemistry with Machine Learning: From Physics-Based to Data-Derived Models and Back<\/i>, Computational Chemistry Network Group Seminar, Pfizer, virtual, Oct 2020<\/b>. (invited talk)<\/li>\n
  25. J. Hachmann, From High-Throughput Computational Chemistry and Molecular Pattern Recognition to the Targeted Design of Novel Chemistry<\/i>, 260th ACS National Meeting, CINF Division Symposium on From Bench to Market: Leveraging AI & Advanced Computational Methods, virtual, Aug 2020<\/b>. (invited talk)<\/li>\n
  26. J. Hachmann, Advancing Computational Chemistry with Machine Learning: From Physics-Based to Data-Derived Models and Back<\/i>, 260th ACS National Meeting, COMP Division OpenEye Outstanding Junior Faculty Award Session, virtual, Aug 2020<\/b>. (poster)<\/li>\n
  27. A. Pradhan, Tailor-Made Materials: Inverse Engineering Compounds Using Feature Correlation<\/i>, University at Buffalo MSc Thesis Defense Seminar, virtual, July 2020<\/b>. (talk)<\/li>\n
  28. K. Pratidar, Predicting Spin Symmetry Breaking in Organic Photovoltaic Compounds Using Data Mining<\/i>, University at Buffalo MSc Thesis Defense Seminar, virtual, May 2020<\/b>. (talk)<\/li>\n
  29. J. Hachmann, A Machine Learning Shortcut to Physics-Based Molecular Modeling<\/i>, Department Seminar, Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh (PA), Mar 2020<\/b>. (invited talk)<\/li>\n
  30. J. Hachmann, The Machine Learning Route to Accelerated Discovery and Inverse Design<\/i>, NIST Materials Genome Initiative Seminar, National Institute of Standards and Technology, Gaithersburg (MD), Feb 2020<\/b>. (invited talk)<\/li>\n
  31. J. Hachmann, Making Machine Learning Work in Chemical and Materials Research<\/i>, Site Seminar, US Army Research Laboratory, Aberdeen Proving Ground (MD), Jan 2020<\/b>. (invited talk)<\/li>\n
  32. J. Hachmann, The Machine Learning Route to Accelerated Discovery and Inverse Design<\/i>, 2019 MRS Fall Meeting, Symposium on Closing the Loop – Using Machine Learning in High-Throughput Discovery of New Materials, Boston (MA), Dec 2019<\/b>. (invited talk)<\/li>\n
  33. J. Hachmann, Making Machine Learning Work in Chemistry<\/i>, NSF MolSSI Workshop on Machine Learning and Chemistry: Challenges on the Way Forward, College Park (MD), Nov 2019<\/b>. (invited talk)<\/li>\n
  34. J. Hachmann, A Deep Neural Network Model for MD-Level Packing Density Predictions and Its Application in the Study of 1.5 Million Organic Molecules<\/i>, 2019 AIChE Annual Meeting, CoMSEF Symposium on Practical Applications of Computational Chemistry and Molecular Simulation, Orlando (FL), Nov 2019<\/b>. (talk)<\/li>\n
  35. J. Hachmann, Advancing Machine Learning Methodology for the Chemical and Materials Domain<\/i>, 2019 AIChE Annual Meeting, Topical Conference on Applications of Data Science to Molecules and Materials, Symposium on Innovations in Methods of Data Science, Orlando (FL), Nov 2019<\/b>. (talk)<\/li>\n
  36. J. Hachmann, Machine Learning for Molecular Property Predictions and the Software Ecosystem That Enables It<\/i>, 2019 AIChE Annual Meeting, CoMSEF Symposium on Software Engineering in and for the Molecular Sciences, Orlando (FL), Nov 2019<\/b>. (talk)<\/li>\n
  37. J. Hachmann, Machine Learning for Molecular Property Predictions and Design<\/i>, Workshop on Machine Learning, Korea Advanced Institute of Science and Technology, Daejeon (South Korea), Nov 2019<\/b>. (invited talk)<\/li>\n
  38. J. Hachmann, Making Machine Learning Work in Chemistry<\/i>, 5th International Conference on Molecular Simulation, Symposium on Big Data and Machine Learning, Jeju (South Korea), Nov 2019<\/b>. (invited talk)<\/li>\n
  39. Y. Pal, High-Throughput Screening of Porphyrin Systems for Artificial Photosynthesis<\/i>, 22nd Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2019<\/b>. (poster)<\/li>\n
  40. K. Patidar, Classifying Organic Photovoltaic Compounds Based on the Existence of Spin Contamination<\/i>, 22nd Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2019<\/b>. (poster)<\/li>\n
  41. G. Vishwakarma, Traversing Energy Barriers: A Virtual High-Throughput Screening Study for Regenerative Energy Systems<\/i>, 22nd Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2019<\/b>. (poster)<\/li>\n
  42. A. Sonpal, Machine Learning Models for Hansen Solubility Parameters and their Application in Predicting Solvent-Polymer Interactions<\/i>, 22nd Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2019<\/b>. (poster)<\/li>\n
  43. A. Pradhan, One Step Closer to Tailor-Made Materials – The Big Data Way<\/i>, 22nd Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2019<\/b>. (poster)<\/li>\n
  44. J. Hachmann, Making Machine Learning Work in Chemistry<\/i>, Machine Learning Seminar, Michigan State University, East Lansing (MI), Sep 2019<\/b>. (invited talk)<\/li>\n
  45. J. Hachmann, Making Machine Learning Work in Chemistry<\/i>, Institute for Advanced Computational Science Seminar, Stony Brook (NY), Sep 2019<\/b>. (invited talk)<\/li>\n
  46. J. Hachmann, Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Materials Modeling and Informatics<\/i>, 258th ACS National Meeting, CINF Division Symposium on Materials Informatics, San Diego (CA), Aug 2019<\/b>. (talk)<\/li>\n
  47. J. Hachmann, A Deep Neural Network Model for MD-Level Packing Density Predictions and its Application in the Study of 1.5 Million Organic Molecules<\/i>, 258th ACS National Meeting, CINF Division Symposium on Machine Learning & Artificial Intelligence in Computational Chemistry, San Diego (CA), Aug 2019<\/b>. (talk)<\/li>\n
  48. J. Hachmann, Machine Learning for Molecular Property Predictions and a Software Ecosystem that Enables It<\/i>, Foundations of Process Analytics and Machine Learning (FOPAM), Raleigh (NC), Aug 2019<\/b>. (poster)<\/li>\n
  49. J. Hachmann, Panel Discussion on Materials Design<\/i>, Foundations of Process Analytics and Machine Learning (FOPAM), Session on Materials Design, Raleigh (NC), Aug 2019<\/b>. (invited panel chair)<\/li>\n
  50. M. Haghighatlari, ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data<\/i>, 2019 MolSSI Software Summer School, Austin (TX), Jul 2019<\/b>. (invited talk)<\/li>\n
  51. J. Hachmann, Machine Learning the Structure-Property Relationships that Define Chemistry<\/i>, Theoretical Chemistry Seminar, University of Colorado Boulder, Boulder (CO), Jul 2019<\/b>. (invited talk)<\/li>\n
  52. J. Hachmann, Computational and Data Science Education in Chemical Engineering<\/i>, CACHE Conference on The Future of Cyber-Assisted Chemical Engineering Education, Breckenridge (CO), Jul 2019<\/b>. (invited talk and poster)<\/li>\n
  53. J. Hachmann, A Machine Learning Shortcut to Physics-Based Modeling and Simulations<\/i>, 10th Congress of the International Society of Theoretical Chemical Physics, Symposium on Machine Learning and Data-Driven Approaches in Chemical Physics, Troms\u00f8 (Norway), Jul 2019<\/b>. (invited talk)<\/li>\n
  54. M. Haghighatlari, Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies<\/i>, University at Buffalo PhD Dissertation Defense Seminar, Buffalo (NY), Jul 2019<\/b>. (talk)<\/li>\n
  55. M. Haghighatlari, Robust and Generalized Structure-Property Relationships by Redesigning Machine Learning Training Sets<\/i>, Reunion Conference for IPAM’s Workshop on Machine Learning for Many-Particle Systems, San Bernardino (CA), Jun 2019<\/b>. (talk)<\/li>\n
  56. J. Hachmann, Panel Discussion on Reproducibility<\/i>, Machine Learning in Science and Engineering, Atlanta (GA), Jun 2019<\/b>. (invited panelist)<\/li>\n
  57. A. Sonpal, Machine Learning Models for Hansen Solubility Parameters and their Application in Predicting Solvent-Polymer Interactions<\/i>, Machine Learning in Science and Engineering, Atlanta (GA), Jun 2019<\/b>. (poster)<\/li>\n
  58. G. Vishwakarma, Tailoring Genetic Algorithm for Data-Driven Research in Chemistry<\/i>, Machine Learning in Science and Engineering, Atlanta (GA), Jun 2019<\/b>. (poster)<\/li>\n
  59. J. Hachmann, Advancing Machine Learning Methodology for Chemistry<\/i>, Machine Learning in Science and Engineering, Symposium on Machine Learning in Chemistry, Biochemistry, and Materials, Atlanta (GA), Jun 2019<\/b>. (invited talk)<\/li>\n
  60. J. Hachmann, Advancing Machine Learning for Chemical and Materials Research<\/i>, ExxonMobil Research and Engineering Seminar, Baytown (TX), May 2019<\/b>. (invited talk)<\/li>\n
  61. J.A. Dudwadkar, Virtual Molecular Library Generation via a Graphical User Interface<\/i>, University at Buffalo MSc Project Defense Seminar, Buffalo (NY), May 2019<\/b>. (talk)<\/li>\n
  62. K. Mukherjee, Computational Modeling of Carboxylic-Based Organic Molecules for Li-Ion Battery Anode Materials<\/i>, University at Buffalo MSc Thesis Defense Seminar, Buffalo (NY), May 2019<\/b>. (talk)<\/li>\n
  63. M. Haghighatlari, Machine Learning for Molecular Modeling and Discovery<\/i>, 2019 UB CBE Graduate Student Seminar, Buffalo (NY), May 2019<\/b>. (invited talk)<\/li>\n
  64. M. Haghighatlari, Machine Learning the Structure-Property Relationships that Determine Chemistry<\/i>, MIT Research Seminar, Cambridge (MA), Apr 2019<\/b>. (invited talk)<\/li>\n
  65. J. Hachmann, Advancing the Software Foundations for Materials Informatics<\/i>, UB CMI Faculty Appreciation Reception, Buffalo (NY), Apr 2019<\/b>. (talk)<\/li>\n
  66. J. Hachmann, Machine Learning in Chemical Research<\/i>, 5th Annual UB CDSE Days, Buffalo (NY), Apr 2019<\/b>. (talk)<\/li>\n
  67. J. Hachmann, A CAREER in Computational and Data-Driven Chemistry<\/i>, NSF CISE CAREER Workshop, Alexandria (VA), Apr 2019<\/b>. (invited talk)<\/li>\n
  68. J. Hachmann, How to Make Data Science Work in the Chemical and Materials Domain<\/i>, 2nd Annual Workshop on Machine Learning in Materials Science, Houston (TX), Apr 2019<\/b>. (invited talk)<\/li>\n
  69. J. Hachmann, Modeling, Virtual High-Throughput Screening, and Machine Learning of Deep Eutectic Solvents<\/i>, 257th ACS National Meeting, PHYS Division Symposium on Structure & Dynamics of Electrolytes: From the Bulk to Interfaces, Orlando (FL), Apr 2019<\/b>. (talk)<\/li>\n
  70. M. Haghighatlari, Advancing Molecular Feature Representation and Machine Learning Design Methodologies Using the ChemML Program Suite<\/i>, 257th ACS National Meeting, PHYS Division Symposium on Sustainable Software for Computational Molecular Science, Orlando (FL), Apr 2019<\/b>. (talk)<\/li>\n
  71. M. Haghighatlari, A Novel Active Learning Approach for Deep Learning of Chemical Data: Extracting More Chemical Insights by Choosing Less<\/i>, 257th ACS National Meeting, CINF Division Symposium on Deep Learning, Orlando (FL), Apr 2019<\/b>. (talk)<\/li>\n
  72. J. Hachmann, Machine Learning the Structure-Property Relationships that Define Chemistry<\/i>, Department Seminar, Department of Chemistry, University of Memphis, Memphis (TN), Mar 2019<\/b>. (invited talk)<\/li>\n
  73. J. Hachmann, Machine Learning the Structure-Property Relationships that Define Chemistry<\/i>, Department Seminar, Department of Chemical Engineering, University of Rochester, Rochester (NY), Jan 2019<\/b>. (invited talk)<\/li>\n
  74. A.R. Mahajan, Computational Modelling of Liquid Organic Hydrogen Carriers<\/i>, University at Buffalo MSc Project Defense Seminar, Buffalo (NY), Jan 2019<\/b>. (talk)<\/li>\n
  75. M. Haghighatlari, Accelerated Discovery of High-Refractive-Index Materials, Using Molecular Modeling and Machine Learning<\/i>, IPAM Seminar Series, Los Angeles (CA), Nov 2018<\/b>. (talk)<\/li>\n
  76. M. Haghighatlari, Redesigning Machine Learning Training Sets Based on Chemical Intuition<\/i>, IPAM Long Program on Science at Extreme Scales: Where Big Data Meets Large-Scale Computing, Los Angeles (CA), Dec 2018<\/b>. (talk)<\/li>\n
  77. J. Hachmann, Making Data-Driven In Silico Research a Mainstream Chemical Engineering Tool<\/i>, 2018 AIChE Annual Meeting, CoMSEF Symposium on Making Molecular Simulation a Mainstream Chemical Engineering Tool, Pittsburgh (PA), Nov 2018<\/b>. (talk)<\/li>\n
  78. M.A.F. Afzal, ChemLG – a Smart and Massively Parallel Code to Accelerate the Molecular Library Generation<\/i>, 2018 AIChE Annual Meeting, CoMSEF Symposium on Software Engineering in and for the Molecular Sciences, Pittsburgh (PA), Nov 2018<\/b>. (talk)<\/li>\n
  79. Y. Pal, High-Throughput In Silico Screening of Candidate Compounds for Deep Eutectic Solvents<\/i>, 2018 AIChE Annual Meeting, CoMSEF Poster Session, Pittsburgh (PA), Oct 2018<\/b>. (poster)<\/li>\n
  80. M. Haghighatlari, Applications of the ChemML Program Suite in Predicting Properties of Organic Materials: A Path to Data-Driven Discovery in Chemistry<\/i>, 2018 AIChE Annual Meeting, CoMSEF Poster Session, Pittsburgh (PA), Oct 2018<\/b>. (poster)<\/li>\n
  81. M.A.F. Afzal, In Silico Exploration of Polyimides with High Index of Refraction Using Molecular Modeling and High-Throughput Screening<\/i>, 2018 AIChE Annual Meeting, MESD Symposium on New Methods in Polymer Modeling, Pittsburgh (PA), Oct 2018<\/b>. (talk)<\/li>\n
  82. G. Vishwakarma, Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers<\/i>, 21st Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2018<\/b>. (poster)<\/li>\n
  83. Y. Pal, High-Throughput In Silico Screening of Candidate Compounds for Deep Eutectic Solvents<\/i>, 21st Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2018<\/b>. (poster)<\/li>\n
  84. A.R. Mahajan, Computational Modelling of Liquid Organic Hydrogen Carriers (LOHCs)<\/i>, 21st Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2018<\/b>. (poster)<\/li>\n
  85. K. Mukherjee, Computational Investigations into the Functionalization of Naphthalenetetracarboxylic Anhydride and its Derivatives as an Anode for Li Ion Batteries<\/i>, 21st Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2018<\/b>. (poster)<\/li>\n
  86. J.A. Dudwadkar, Graphical User Interface for the ChemLG Molecular Library Generator<\/i>, 21st Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2018<\/b>. (poster)<\/li>\n
  87. A. Sonpal, Data-Driven Approach to Predict Melting Points of Deep Eutectic Solvents<\/i>, 21st Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2018<\/b>. (poster)<\/li>\n
  88. M. Haghighatlari, Accelerated Discovery of High-Refractive-Index Materials, Using Molecular Modeling and Machine Learning <\/i>, IPAM Long Program on Science at Extreme Scales: Where Big Data Meets Large-Scale Computing, Los Angeles (CA), Oct 2018<\/b>. (talk)<\/li>\n
  89. J. Hachmann, Machine Learning for Molecular Property Predictions and Rational Design in Chemistry<\/i>, Workshop on Machine Learning at the Graduate Center of CUNY, New York (NY), Sep 2018<\/b>. (invited talk)<\/li>\n
  90. J. Hachmann, Revolutionizing Molecular Modeling with Machine Learning<\/i>, 256th ACS National Meeting, COMP Division Symposium on Revolutionizing Chemical Sciences with Artificial Intelligence, Boston (MA), Aug 2018<\/b>. (invited talk)<\/li>\n
  91. A. Sonpal, Predicting Melting Points of Deep Eutectic Solvents<\/i>, University at Buffalo MSc Thesis Defense Seminar, Buffalo (NY), Aug 2018<\/b>. (talk)<\/li>\n
  92. P.-H. Chen, Using Concepts from Active Learning and Applicability Domain to Enhance Predictive Modeling<\/i>, University at Buffalo MSc Project Defense Seminar, Buffalo (NY), Aug 2018<\/b>. (talk)<\/li>\n
  93. J. Hachmann, Advancing Molecular Property Predictions and Design with Machine Learning<\/i>, Lawrence Berkeley National Laboratory, Department Seminar, Berkeley (CA), Aug 2018<\/b>. (invited talk)<\/li>\n
  94. G. Vishwakarma, Machine Learning Model Selection for Predicting Properties of Organic Polymers<\/i>, University at Buffalo MSc Thesis Defense Seminar, Buffalo (NY), Jul 2018<\/b>. (talk)<\/li>\n
  95. M.A.F. Afzal, Large-Scale Exploration of Chemical Moieties for the Design of Next-Generation High-Refractive-Index Polymers<\/i>, 2018 Conference on Foundations of Molecular Modeling and Simulation (FOMMS 2018) \u2013 Innovations for Complex Systems, Delevan (WI), Jul 2018<\/b>. (poster)<\/li>\n
  96. M. Haghighatlari, ChemML: A Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences<\/i>, Reunion Conference for IPAM’s Workshop on Machine Learning for Many-Particle Systems, San Bernardino (CA), Jun 2018<\/b>. (talk)<\/li>\n
  97. M.A.F. Afzal, Exploration of Chemical Space to Identify Exceptional Molecular Targets for Optical Applications<\/i>, Reunion Conference for IPAM’s Workshop on Machine Learning for Many-Particle Systems, San Bernardino (CA), Jun 2018<\/b>. (talk)<\/li>\n
  98. M.A.F. Afzal, Harnessing Virtual High-Throughput Screening and Machine Learning for the Discovery of Novel High-Refractive-Index Polymers<\/i>, Machine Learning in Science and Engineering, Symposium on Predicting Molecular Properties and Molecular Design, Pittsburgh (PA), Jun 2018<\/b>. (poster)<\/li>\n
  99. J. Hachmann, Advancing Molecular Property Predictions and Design with Machine Learning<\/i>, Machine Learning in Science and Engineering, Symposium on Predicting Molecular Properties and Molecular Design, Pittsburgh (PA), Jun 2018<\/b>. (invited talk)<\/li>\n
  100. M. Haghighatlari, Software Development and its Application for Predicting Optical Properties in Molecular Space: ChemML Program Suite<\/i>, Machine Learning in Science and Engineering, Symposium on Predicting Molecular Properties and Molecular Design, Pittsburgh (PA), Jun 2018<\/b>. (poster)<\/li>\n
  101. S. Sivaraj, ChemBDDB: A Software-Assisted Database Infrastructure for the Exploration of Chemical and Materials Space<\/i>, University at Buffalo MSc Project Defense Seminar, Buffalo (NY), May 2018<\/b>. (talk)<\/li>\n
  102. J. Hachmann, Advancing a Data-Driven in silico Research Paradigm in the Chemical and Materials Domain<\/i>, 2018 TechConnect World Innovation Conference, Symposium on Informatics, Modeling, and Simulation, Anaheim (CA), May 2018<\/b>. (invited talk)<\/li>\n
  103. M.A.F. Afzal, From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications<\/i>, University at Buffalo PhD Dissertation Defense Seminar, Buffalo (NY), May 2018<\/b>. (talk)<\/li>\n
  104. J. Hachmann, Machine Learning in Chemistry<\/i>, Dean\u2019s Advisory Council Meeting, Buffalo (NY), Apr 2018<\/b>. (invited talk)<\/li>\n
  105. M. Haghighatlari, Deep Learning the Refractive Index of Organic Materials<\/i>, School of Engineering and Applied Sciences Graduate Research Poster Competition, University at Buffalo, Buffalo (NY), Mar 2018<\/b>. (invited poster)<\/li>\n
  106. V. Kumaran Sudalayandi Rajeswari, Modeling Polymer Degradation and First-Principles Virtual High-Throughput Screening<\/i>, University at Buffalo MSc Thesis Defense Seminar, Buffalo (NY), Jan 2018<\/b>. (talk)<\/li>\n
  107. M. Haghighatlari, J. Hachmann, Hands-on Machine Learning with Python<\/i>, APS GSOFT Division Short Course on Machine Learning and Data Science in Soft Matter, Los Angeles (CA), Mar 2018<\/b>. (invited talk)<\/li>\n
  108. J. Hachmann, Machine Learning the Structure-Property Relationships that Define Chemistry<\/i>, Humboldt Kolleg on New Vistas in Molecular Thermodynamics, Session on Fundamentals of Chemical Design and Engineering, Berkeley (CA), Jan 2018<\/b>. (invited talk)<\/li>\n
  109. M.A.F. Afzal, Accelerated the Discovery of Polymers using Molecular Modeling, Virtual High-Throughput Screening, and Machine Learning<\/i>, ExxonMobil Research and Engineering Seminar, Clinton (NJ), Nov 2017<\/b>. (invited talk)<\/li>\n
  110. J. Hachmann, Harnessing Virtual High-Throughput Screening and Machine Learning for the Discovery of Novel High Refractive Index Polymers<\/i>, 2017 AIChE Annual Meeting, Symposium on Atomistic and Molecular Modeling and Simulation of Polymers, Minneapolis (MN), Nov 2017<\/b>. (talk)<\/li>\n
  111. J. Hachmann, Advancing Molecular Simulation Methods with Machine Learning<\/i>, 2017 AIChE Annual Meeting, Symposium on Recent Advances in Molecular Simulation Methods, Minneapolis (MN), Nov 2017<\/b>. (talk)<\/li>\n
  112. J. Hachmann, A Data-Driven In Silico Research Paradigm for the Rational Design of Catalyst Systems and the Exploration of Chemical Space<\/i>, 2017 AIChE Annual Meeting, Symposium on New Developments in Computational Catalysis, Minneapolis (MN), Oct 2017<\/b>. (talk)<\/li>\n
  113. J. Hachmann, Data-Driven In Silico Research in the Hachmann Group<\/i>, UB CMI Faculty Appreciation Reception, Buffalo (NY), Sep 2017<\/b>. (talk)<\/li>\n
  114. A. Sonpal, Molecular Dynamics Approach to Calculate Melting Point of Hydrogen Bond Donors for Deep Eutectic Solvents<\/i>, 20th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2017<\/b>. (poster)<\/li>\n
  115. P.-H. Chen, Developing a ChemML GUI Using Web Frameworks<\/i>, 20th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2017<\/b>. (poster)<\/li>\n
  116. S. Sivaraj, ChemBDDB: A Database Infrastructure for the Exploration of Chemical and Material Space<\/i>, 20th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2017<\/b>. (poster)<\/li>\n
  117. A. Rajendra Mahajan, Computational Modeling and Virtual High-Throughput Screening of LOHCs<\/i>, 20th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2017<\/b>. (poster)<\/li>\n
  118. G. Vishwakarma, Neural Network Model Selection using Genetic Algorithm for Prediction of Molecular Properties<\/i>, 20th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2017<\/b>. (poster)<\/li>\n
  119. Y. Pal, Combinatorial Computational Studies towards Advances in Lithium Ion Battery Technologies<\/i>, 20th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2017<\/b>. (poster)<\/li>\n
  120. M. Haghighatlari, Deep Learning of Refractive Index of Organic Materials<\/i>, 20th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2017<\/b>. (poster)<\/li>\n
  121. M.A.F. Afzal, Accelerated the Discovery of Polymers using Molecular Modeling, Virtual High-Throughput Screening, and Machine Learning<\/i>, ExxonMobil Research and Engineering Seminar, Baytown (TX), Sep 2017<\/b>. (invited talk)<\/li>\n
  122. J. Hachmann, A Roadmap to Data-Driven Discovery and Rational Design in Chemical and Materials Research<\/i>, Seminar for the Center for Nonlinear Studies at Los Alamos National Laboratory, Los Alamos (NM), Sep 2017<\/b>. (invited talk)<\/li>\n
  123. M.A.F. Afzal, Virtual High-Throughput Infrastructure for the Accelerated Discovery of Organic Materials<\/i>, 254th ACS National Meeting, COMP Division Poster Session, Washington (DC), Aug 2017<\/b>. (poster)<\/li>\n
  124. M.A.F. Afzal, Discovering Polyimides with Exceptional Optical Properties using First-Principles Modeling, Virtual High-Throughput Screening, and Machine Learning<\/i>, 254th ACS National Meeting, Sci-Mix Poster Session, Washington (DC), Aug 2017<\/b>. (poster)<\/li>\n
  125. M.A.F. Afzal, Discovering Polyimides with Exceptional Optical Properties using First-Principles Modeling, Virtual High-Throughput Screening, and Machine Learning<\/i>, 254th ACS National Meeting, COMP Division Poster Session, Washington (DC), Aug 2017<\/b>. (poster)<\/li>\n
  126. M.A.F. Afzal, Machine Learning Approach for the Fast and Accurate Prediction of Optical Properties of Organic Molecules<\/i>, 254th ACS National Meeting, CINF Division Scholarship for Scientific Excellence Poster Session, Washington (DC), Aug 2017<\/b>. (poster)<\/li>\n
  127. M.A.F. Afzal, Discovering Polymers with Exceptional Optical Properties Using First-Principles Modeling, Virtual High-Throughput Screening, and Machine Learning<\/i>, Samsung Research Seminar, Boston (MA), Jul 2017<\/b>. (invited talk)<\/li>\n
  128. S. Brown, Applicability Domain of Machine Learning Models for the Structurally Limited Chemical Data Sets Generated by Virtual High-Throughput Screening<\/i>, 23rd McNair Scholars Research Conference, Niagara Falls (NY), Jul 2017<\/b>. (talk)<\/li>\n
  129. S. Brown, Applicability Domain of Machine Learning Models for the Structurally Limited Chemical Data Sets Generated by Virtual High-Throughput Screening<\/i>, 23rd McNair Scholars Research Conference, Niagara Falls (NY), Jul 2017<\/b>. (poster)<\/li>\n
  130. S. Brown, Applicability Domain of Machine Learning Models for the Structurally Limited Chemical Data Sets Generated by Virtual High-Throughput Screening<\/i>, 2017 Louis Stokes Alliances for Minority Participation Summer Research Program Symposium, Buffalo (NY), Jul 2017<\/b>. (poster)<\/li>\n
  131. J. Hachmann, How to Make Data Science Work for Chemistry?<\/i>, Chemical Sciences Roundtable of the National Academy of Sciences, Panel on Data Science in Chemistry and Chemical Engineering, Washington (DC), Jul 2017<\/b>. (invited talk)<\/li>\n
  132. J. Hachmann, Rational Materials Design via Machine Learning<\/i>, 253rd ACS National Meeting, CINF Division Symposium on Materials Informatics and Computational Modeling, San Francisco (CA), Apr 2017<\/b>. (invited talk)<\/li>\n
  133. J. Hachmann, A Data-Driven In Silico Research Paradigm for the Rational Design of Catalyst Systems<\/i>, 253rd ACS National Meeting, CATL Division Symposium on Designed Catalysis: Materials Genome Approach to Heterogeneous Processes, San Francisco (CA), Apr 2017<\/b>. (invited talk)<\/li>\n
  134. M.A.F. Afzal, Accelerated Discovery of High-Refractive-Index Polymers Using First-Principles Modeling, Virtual High-Throughput Screening, and Data Mining<\/i>, APS March Meeting 2017, New Orleans (LA), Mar 2017<\/b>. (poster)<\/li>\n
  135. M.A.F. Afzal, ChemHTPS – A Virtual High-Throughput Screening Program Suite for the Chemical and Materials Sciences<\/i>, APS March Meeting 2017, DMP\/DCOMP Division Symposium on Computational Discovery and Design of Novel Materials, New Orleans (LA), Mar 2017<\/b>. (talk)<\/li>\n
  136. M.A.F. Afzal, Accelerated Discovery of High-Refractive Index Polymers<\/i>, IPAM Workshop on Synergies between Machine Learning and Physical Models, Los Angeles (CA), Dec 2016<\/b>. (poster)<\/li>\n
  137. M. Haghighatlari, ChemML: A Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences<\/i>, IPAM Workshop on Synergies between Machine Learning and Physical Models, Los Angeles (CA), Dec 2016<\/b>. (poster)<\/li>\n
  138. J. Hachmann, A Software Ecosystem for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space<\/i>, IPAM Workshop on Synergies between Machine Learning and Physical Models, Los Angeles (CA), Dec 2016<\/b>. (invited talk)<\/li>\n
  139. M. Haghighatlari, From Structural Analysis to Fingerprints for Molecular Property Predictions<\/i>, IPAM Long Program on Understanding Many-Particle Systems with Machine Learning, Los Angeles (CA), Nov 2016<\/b>. (talk)<\/li>\n
  140. J. Hachmann, A Software Ecosystem for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space<\/i>, 2016 AIChE Annual Meeting, CoMSEF Symposium on Software Engineering in and for the Molecular Sciences, San Francisco (CA), Nov 2016<\/b>. (poster)<\/li>\n
  141. Y. Pal, Modeling Protocols for ORR and OER Catalysts in Solar Water Splitting and Fuel Cell Reactions<\/i>, 19th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2016<\/b>. (poster)<\/li>\n
  142. K.S. Rajeswari Vigneshwar, Modeling Degradation of Polymers<\/i>, 19th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2016<\/b>. (poster)<\/li>\n
  143. M. Haghighatlari, ChemML: A Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences<\/i>, 19th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2016<\/b>. (poster)<\/li>\n
  144. M.A.F. Afzal, Accelerated Discovery of High-Refractive-Index Polyimides<\/i>, 19th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2016<\/b>. (poster)<\/li>\n
  145. J. Hachmann, Modeling Biodegradation of Polymeric Materials<\/i>, UB CMI Faculty Appreciation Reception, Buffalo (NY), Sep 2016<\/b>. (talk)<\/li>\n
  146. J. Hachmann, A Software Ecosystem for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space<\/i>, Theory and Applications of Computational Chemistry 2016 (TACC 2016), Seattle (WA), Aug 2016<\/b>. (invited talk)<\/li>\n
  147. M. Haghighatlari, Trend-Based Feature Selection in Molecular Descriptor Space<\/i>, 252nd ACS National Meeting, PHYS Division Poster Session, Philadelphia (PA), Aug 2016<\/b>. (poster)<\/li>\n
  148. Y. Pal, Modeling Protocols for ORR and OER Catalysts in Solar Water Splitting<\/i>, 252nd ACS National Meeting, PHYS Division Poster Session, Philadelphia (PA), Aug 2016<\/b>. (poster)<\/li>\n
  149. M. Haghighatlari, From Structural Analysis to Fingerprints for Molecular Property Predictions<\/i>, 252nd ACS National Meeting, PHYS Division Symposium on Accelerating Discovery: Citizen Science, Big Data, and Machine Learning for Physical Chemistry, Philadelphia (PA), Aug 2016<\/b>. (talk)<\/li>\n
  150. M.A.F. Afzal, Accelerated Discovery of High-Refractive-Index Polymers using First-Principles Modeling, Virtual High-Throughput Screening, and Data Mining<\/i>, 252nd ACS National Meeting, COMP Division Poster Session, Philadelphia (PA), Aug 2016<\/b>. (poster)<\/li>\n
  151. M. Haghighatlari, ChemML: A Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences<\/i>, 252nd ACS National Meeting, Sci-Mix Poster Session, Philadelphia (PA), Aug 2016<\/b>. (poster)<\/li>\n
  152. M. Haghighatlari, ChemML: A Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences<\/i>, 252nd ACS National Meeting, CINF Division Scholarship for Scientific Excellence Poster Session, Philadelphia (PA), Aug 2016<\/b>. (poster)<\/li>\n
  153. J. Hachmann, A Software Ecosystem for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space<\/i>, 252nd ACS National Meeting, COMP Division Symposium on Emerging Technologies in Computational Chemistry, Philadelphia (PA), Aug 2016<\/b>. (invited talk)<\/li>\n
  154. M.A.F. Afzal, ChemLG \u2013 A Smart and Massively Parallel Molecule Library Generator<\/i>, 252nd ACS National Meeting, COMP Division Symposium on Designing Chemical Libraries for Screening, Philadelphia (PA), Aug 2016<\/b>. (talk)<\/li>\n
  155. Y. Tian, Inheritance of Molecular Orbital Energies from Monomer Building Blocks to Larger Copolymers in Organic Semiconductors<\/i>, University at Buffalo MSc Thesis Defense Seminar, Buffalo (NY), Aug 2016<\/b>. (talk)<\/li>\n
  156. J. Hachmann, A Software Ecosystem for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space<\/i>, 48th Midwest Theoretical Chemistry Conference (MWTCC 2016), Pittsburgh (PA), Jun 2016<\/b>. (talk)<\/li>\n
  157. Y. Pal, Modeling Protocols for ORR and OER catalysts in Solar Water Splitting and Fuel Cell Reactions<\/i>, 48th Midwest Theoretical Chemistry Conference (MWTCC 2016), Pittsburgh (PA), Jun 2016<\/b>. (poster)<\/li>\n
  158. M. Haghighatlari, Substructure Based Fingerprints for Molecular Property Predictions<\/i>, 48th Midwest Theoretical Chemistry Conference (MWTCC 2016), Pittsburgh (PA), Jun 2016<\/b>. (poster)<\/li>\n
  159. M.A.F. Afzal, Accelerated Discovery of High-Refractive-Index Polymers Using First-Principles Modeling, Virtual High-Throughput Screening, and Data Mining<\/i>, 48th Midwest Theoretical Chemistry Conference (MWTCC 2016), Pittsburgh (PA), Jun 2016<\/b>. (poster)<\/li>\n
  160. M.A.F. Afzal, Accelerated Discovery of Organic Polymers for Optical Applications<\/i>, 2016 UB CBE Graduate Student Seminar, Buffalo (NY), May 2016<\/b>. (invited talk)<\/li>\n
  161. J. Hachmann, Big Data and Machine Learning in Chemical and Materials Research<\/i>, 2016 UB CDSE Days, Buffalo (NY), Mar 2016<\/b>. (talk)<\/li>\n
  162. M.A.F. Afzal, Accurate Prediction of the Refractive Index of Polymers Using First-Principles and Data Modeling<\/i>, APS March Meeting 2016, Baltimore (MD), Mar 2016<\/b>. (poster)<\/li>\n
  163. J. Hachmann, Computational and Data-Driven Discovery of Novel High-Refractive-Index Polymers<\/i>, 251st ACS National Meeting, PMSE Division Symposium on Computation and Cheminformatics in Polymers Research, San Diego (CA), Mar 2016<\/b>. (invited talk)<\/li>\n
  164. J. Hachmann, Panel Discussion: The Materials Genome and Materials Informatics<\/i>, 251st ACS National Meeting, Symposium on Computational Material Science: Theory meets Experiment, San Diego (CA), Mar 2016<\/b>. (invited talk)<\/li>\n
  165. J. Hachmann, Data-Driven Research and a Rational Design Paradigm in the Chemical and Materials Disciplines<\/i>, 251st ACS National Meeting, Symposium on Computational Material Science: Theory meets Experiment, San Diego (CA), Mar 2016<\/b>. (invited talk)<\/li>\n
  166. J. Hachmann, ChemML \u2013 a Machine Learning and Informatics Toolbox for the Chemical and Materials Sciences<\/i>, International Chemical Congress of Pacific Basin Societies 2015 (Pacifichem 2015), Symposium on Data Mining and Machine Learning Meets Experiment and First-Principles Simulation for Materials Discovery, Honolulu (HI), Dec 2015<\/b>. (invited talk)<\/li>\n
  167. J. Hachmann, Computational and Data-Driven Discovery of Novel High Refractive Index Polymers<\/i>, 2015 AIChE Annual Meeting, MESD Symposium on Atomistic and Molecular Modeling and Simulation of Polymers, Salt Lake City (UT), Nov 2015<\/b>. (talk)<\/li>\n
  168. J. Hachmann, CheML – a Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences<\/i>, 2015 AIChE Annual Meeting, CoMSEF Symposium on Software Engineering in and for the Molecular Sciences, Salt Lake City (UT), Nov 2015<\/b>. (talk)<\/li>\n
  169. J. Hachmann, Rational Materials Design via Machine Learning<\/i>, Department Seminar, Department of Physics, University of Vermont, Burlington (VT), Oct 2015<\/b>. (invited talk)<\/li>\n
  170. Y. Tian, Systematic Trends in Results from Different Density Functional Theory Models<\/i>, 18th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2015<\/b>. (poster)<\/li>\n
  171. Y. Pal, Rational Design of Catalyst Systems for Solar Water Splitting Through Computational Chemistry<\/i>, 18th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2015<\/b>. (poster)<\/li>\n
  172. W.S. Evangelista, ChemHTPS: A Computational High-Throughput Screening Infrastructure for Chemical Research<\/i>, 18th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2015<\/b>. (poster)<\/li>\n
  173. M. Haghighatlari, Trend-Based Feature Selection in Molecular Descriptor Space<\/i>, 18th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2015<\/b>. (poster)<\/li>\n
  174. M.A.F. Afzal, Accurate Prediction of the Refractive Index of Polymers Using First-Principles and Data Modeling<\/i>, 18th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Sep 2015<\/b>. (poster)<\/li>\n
  175. C.-Y. Shih, Systematic Trends in Results from Different Density Functional Theory Models<\/i>, University at Buffalo MSc Thesis Defense Seminar, Buffalo (NY), Aug 2015<\/b>. (talk)<\/li>\n
  176. J. Hachmann, Workshop I: Data Mining, Machine Learning, and Materials Informatics<\/i>, 2015 Conference on Foundations of Molecular Modeling and Simulation (FOMMS 2015) \u2013 Molecular Modeling and the Materials Genome, Mt Hood (OR), Jul 2015<\/b>. (invited talk)<\/li>\n
  177. J. Hachmann, Computing Quantum Chemical Results without Doing Quantum Chemistry: A Machine Learning Shortcut<\/i>, 47th Midwest Theoretical Chemistry Conference (MWTCC 2015), Ann Arbor (MI), Jun 2015<\/b>. (invited talk)<\/li>\n
  178. M. Haghighatlari, Molecular Electronic Properties of Organic Semiconductors<\/i>, 33rd Annual UB Chemistry Graduate Student Symposium, Buffalo (NY), May 2015<\/b>. (poster)<\/li>\n
  179. M.A.F. Afzal, Accelerated Discovery of High Refractive Index Polyimides<\/i>, 33rd Annual UB Chemistry Graduate Student Symposium, Buffalo (NY), May 2015<\/b>. (poster)<\/li>\n
  180. J. Hachmann, Research in the Hachmann Group<\/i>, UB CBE Advisory Board Meeting, Buffalo (NY), Apr 2015<\/b>. (talk)<\/li>\n
  181. B.A. Moore, Discovering Trends in Data from Different Quantum Chemical Models<\/i>, 11th UB Annual Celebration of Undergraduate Academic Excellence Symposium, Buffalo (NY), Apr 2015<\/b>. (poster)<\/li>\n
  182. J. Hachmann, Research in the Hachmann Group: Computational Catalysis, Materials, Big Data, & More…<\/i>, 2015 UB CDSE Days, Buffalo (NY), Mar 2015<\/b>. (talk)<\/li>\n
  183. J. Hachmann, Molecular Properties from Big Data<\/i>, IPAM Workshop on Machine Learning for Many-Particle Systems, Los Angeles (CA), Feb 2015<\/b>. (invited talk)<\/li>\n
  184. M. Haghighatlari, Machine Learning for the Prediction of Molecular Properties<\/i>, IPAM Workshop on Machine Learning for Many-Particle Systems, Los Angeles (CA), Feb 2015<\/b>. (poster)<\/li>\n
  185. J. Hachmann, Molecular Properties from Big Data<\/i>, Workshop on Opportunities in Materials Informatics, Madison (WI), Feb 2015<\/b>. (poster)<\/li>\n
  186. J. Hachmann, Molecular Properties of Organic Semiconductors from Big Data<\/i>, MRS Fall Meeting, Symposium on Fundamentals of Organic Semiconductors: Synthesis, Morphology, Devices, and Theory, Boston (MA), Dec 2014<\/b>. (invited talk)<\/li>\n
  187. M. Haghighatlari, Machine Learning for Accelerated Materials Discovery<\/i>, 17th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2014<\/b>. (poster)<\/li>\n
  188. M.A.F. Afzal, Computational Approach to Discovering Novel Monomers for High Refractive Index Organic Materials<\/i>, 17th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2014<\/b>. (poster)<\/li>\n
  189. C.-Y. Shih, Discovering Trends in Data from Different Quantum Chemical Models<\/i>, 17th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2014<\/b>. (poster)<\/li>\n
  190. J. Hachmann, Molecular Properties from Big Data<\/i>, 248th ACS National Meeting, COMP Division Symposium on Quantum Chemical Calculation of Molecular Properties: A Tribute to Professor Nicholas C. Handy, San Francisco (CA), Aug 2014<\/b>. (invited talk)<\/li>\n
  191. J. Hachmann, The Harvard Clean Energy Project – a Virtual High-Throughput Search Framework for New Organic Solar Cell Materials<\/i>, 248th ACS National Meeting, ENFL Division Symposium on Applications of Theoretical Chemistry for Energy and Fuel Production, San Francisco (CA), Aug 2014<\/b>. (invited talk)<\/li>\n
  192. J. Hachmann, Hachmann Group Research Program<\/i>, UB CBE Advisory Board Meeting, Buffalo (NY), Feb 2014. (talk)\n
  193. J. Hachmann, High-Throughput Quantum Chemistry and Big Data Techniques for the Rational Design of Organic Semiconductors<\/i>, Conference on Electronics Materials and Applications 2014 (EMA 2014), Symposium on Computational Design of Electronic Materials, Orlando (FL), Jan 2014<\/b>. (invited talk)<\/li>\n<\/ol>\n
    \n

      <\/p>\n

    Harvard (2009–2014)<\/h4>\n
      \n
    1. J. Hachmann, High-Throughput Quantum Chemistry and Big Data Techniques for the Rational Design of Organic Semiconductors<\/i>, Special Topics in Theoretical Chemistry Seminar, Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, Zurich (Switzerland), Oct 2013<\/b>. (invited talk)<\/li>\n
    2. J. Hachmann, From High-Throughput Quantum Chemistry to the Rational Design of Organic Semiconductors \u2013 a Big Data and Materials Informatics Approach<\/i>, CECAM Workshop on Structure-Property Relationships of Molecular Precursors to Organic Electronics, Lausanne (Switzerland), Oct 2013<\/b>. (invited talk)<\/li>\n
    3. J. Hachmann, High-Throughput Quantum Chemistry and Big Data Techniques for the Rational Design of Organic semiconductors<\/i>, 49th Symposium on Theoretical Chemistry: Bridging Scales in Theoretical Chemistry, Erlangen (Germany), Sep 2013<\/b>. (talk)<\/li>\n
    4. J. Hachmann, The Harvard Clean Energy Project: High-Throughput Screening and Design of Organic Photovoltaic Materials via Automated, First-Principles Quantum Chemistry on the IBM World Community Grid<\/i>, 246th ACS National Meeting, PHYS Division Symposium on Physical Chemistry of Solar Energy Conversion, Indianapolis (IN), Sep 2013<\/b>. (invited talk)<\/li>\n
    5. J. Hachmann, High-Throughput and Big Data Techniques in Computational Materials Science<\/i>, 246th ACS National Meeting, COMP Division Symposium on Chemical Mechanisms in Advanced Materials, Indianapolis (IN), Sep 2013<\/b>. (invited talk)<\/li>\n
    6. J. Hachmann, Harvard Clean Energy Project: From Big Data and Cheminformatics to the Rational Design of Molecular OPV Materials<\/i>, 246th ACS National Meeting, CINF Division Poster Session, Indianapolis (IN), Sep 2013<\/b>. (poster)<\/li>\n
    7. J. Hachmann, Accelerated Discovery of Third-Generation OPV Semiconductors: Screening Molecular Materials on the World Community Grid<\/i>, Materials Genome Initiative Principal Investigators’ Meeting, Washington (DC), Jul 2013<\/b>. (poster)<\/li>\n
    8. J. Hachmann, Rational Design of Semiconductors for Organic Photovoltaics via High-Throughput Quantum Chemistry and Materials Informatics<\/i>, Department Seminar, School of Chemistry, University of Edinburgh, Edinburgh (Scotland), May 2013<\/b>. (invited talk)<\/li>\n
    9. J. Hachmann, Rationales Design von Halbleitern f\u00fcr organische Solarzellen durch High-Throughput Quantenchemie und Materialinformatik<\/i>, Theoretical Chemistry Colloquium, Institute of Physical and Theoretical Chemistry, Braunschweig University of Technology, Braunschweig (Germany), May 2013<\/b>. (invited talk)<\/li>\n
    10. J. Hachmann, The Harvard Clean Energy Project: Computational High-Throughput Screening of OPV Materials on the IBM World Community Grid<\/i>, Department Seminar, Department of Chemical and Biological Engineering, University at Buffalo – SUNY, Buffalo (NY), Mar 2013<\/b>. (invited talk)<\/li>\n
    11. J. Hachmann, Automated, High-Throughput Quantum Chemistry: Screening OPV Materials on the World Community Grid<\/i>, 53rd Sanibel Symposium, St. Simons Island (GA), Feb 2013<\/b>. (talk)<\/li>\n
    12. J. Hachmann, The Harvard Clean Energy Project: High-Throughput Ab Initio Screening of OPVs via Distributed Volunteer Computing<\/i>, 2012 MRS Fall Meeting, Symposium on Next-Generation Polymer-based Organic Photovoltaics, Boston (MA), Nov 2012<\/b>. (talk)<\/li>\n
    13. J. Hachmann, Automated, High-Throughput Quantum Chemistry: Screening OPV Materials on the World Community Grid<\/i>, XIVth International Congress of Quantum Chemistry (XIV-ICQC2012), Boulder (CO), Mar 2012<\/b>. (poster)<\/li>\n
    14. J. Hachmann, The Harvard Clean Energy Project: An Automated, High-Throughput, First-Principles Screening of Organic Photovoltaics on the World Community Grid<\/i>, S\u00e9minaire du RQMP Versant Nord, D\u00e9partement de physique, Universit\u00e9 de Montr\u00e9al, Montr\u00e9al (Canada), Mar 2012<\/b>. (invited talk)<\/li>\n
    15. J. Hachmann, Developing Renewable Energy Materials on your Idling Computer: The Harvard Clean Energy Project on the World Community Grid<\/i>, 2012 MIT Energy Conference Showcase, Boston (MA), Mar 2012<\/b>. (poster)<\/li>\n
    16. J. Hachmann, The Harvard Clean Energy Project: High-Throughput Screening of OPVs Using First-Principles Electronic Structure Theory<\/i>, APS March Meeting 2012, DCOMP\/DMP Symposium on Computational Design of Materials – Nanostructured and Energy Materials, Boston (MA), Feb 2012<\/b>. (talk)<\/li>\n
    17. J. Hachmann, The Harvard Clean Energy Project: Automated, Large-Scale, First-Principles Screening of Carbon-Based Photovoltaics on the World Community Grid<\/i>, 20th Conference on Current Trends in Computational Chemistry (CCTCC 20), Jackson (MS), Oct 2011<\/b>. (poster)<\/li>\n
    18. J. Hachmann, Developing Renewable Energy Materials on your Idling Computer: High-Throughput In Silico Screening of OPVs in the Harvard Clean Energy Project<\/i>, Global Climate and Energy Project Research Symposium 2011 (GCEP 2011), Stanford (CA), Oct 2011<\/b>. (poster)<\/li>\n
    19. J. Hachmann, Quantum Chemistry on the World Community Grid: a Large-Scale Screening of Molecular Motifs for OPVs<\/i>, 9th Triennial Congress of the World Association of Theoretical and Computational Chemists 2011 (WATOC 2011), Santiago de Compostela (Spain), Jul 2011<\/b>. (poster)<\/li>\n
    20. J. Hachmann, The Harvard Clean Energy Project: First-Principles Screening of OPV Materials on the World Community Grid<\/i>, APS March Meeting 2011, DCOMP\/DMP Symposium on Computational Materials Design – Data-Driven, Dallas (TX), Mar 2011<\/b>. (talk)<\/li>\n
    21. J. Hachmann, Developing Renewable Energy Materials on your Idling Computer: the Harvard Clean Energy Project on the World Community Grid<\/i>, 2011 MIT Energy Conference Showcase, Boston (MA), Mar 2011<\/b>. (poster)<\/li>\n
    22. J. Hachmann, First-Principles Screening and Design of Organic Photovoltaic Materials on the World Community Grid: The Clean Energy Project<\/i>, Workshop on Wavepackets, Chaos, and Scattering: from Chemistry to Physics and Back, Cambridge (MA), Oct 2010<\/b>. (poster)<\/li>\n
    23. J. Hachmann, The Harvard Clean Energy Project: First-Principles Study of Molecular Motifs for OPVs on the World Community Grid<\/i>, 46th Symposium on Theoretical Chemistry: Quantum Chemistry for Large and Complex Systems – From Theory to Algorithms and Applications, M\u00fcnster (Germany), Sep 2010<\/b>. (talk)<\/li>\n
    24. J. Hachmann, First-Principles Screening and Design of Organic Photovoltaic Materials on the World Community Grid<\/i>, 240th ACS National Meeting, PHYS Division Symposium on Molecular Systems for Efficient Solar Energy Conversion and Storage, Boston (MA), Aug 2010<\/b>. (talk)<\/li>\n
    25. J. Hachmann, The Harvard Clean Energy Project: A Large-Scale Computational Search for New Organic Photovoltaics on the World Community Grid<\/i>, Workshop on Complex Interactions & Mechanisms in Organic Photovoltaics (CIMOPV), Brisbane (Australia), Jul 2010<\/b>. (invited talk)<\/li>\n
    26. J. Hachmann, First-principles Screening and Design of Organic Photovoltaic Materials on the World Community Grid: The Clean Energy Project<\/i>, Molecular Quantum Mechanics 2010: An International Conference in Honor of Professor Henry F. Schaefer III (MQM 2010), Berkeley (CA), May 2010<\/b>. (poster)<\/li>\n
    27. J. Hachmann, The Harvard Clean Energy Project: A Large-Scale Computational Search for New Organic Photovoltaics<\/i>, 3rd Puerto Rico NSF EPSCoR\/RII IFN Annual Meeting, Rio Grande (PR), May 2010<\/b>. (invited talk)<\/li>\n
    28. J. Hachmann, Screening and Design of Renewable Energy Materials on the World Community Grid<\/i>, 2010 MIT Energy Conference Showcase, Boston (MA), Mar 2010<\/b>. (poster)<\/li>\n<\/ol>\n
      \n

        <\/p>\n

      Cornell (2004–2009)<\/h4>\n
        \n
      1. J. Hachmann, Transition Metal Computational Chemistry \u2013 a Study of the 3d-M(smif)2<\/sub> Series \u2013 (and some DMRG)<\/i>, Cornell University PhD Dissertation Defense Seminar, Ithaca (NY), Aug 2009<\/b>. (talk)<\/li>\n
      2. J. Hachmann, Electronic Structure of 3d-M(smif)2<\/sub>: A Case Study for Computational Investigations of Coordination Compounds and their Spectroscopic Characterization<\/i>, XIIIth International Congress of Quantum Chemistry (XIII-ICQC2009), Helsinki (Finland), Jun 2009<\/b>. (poster)<\/li>\n
      3. J. Hachmann, Formal Oxidation States and Realistic Charge Distributions in Transition Metal Chemistry<\/i>, XIIIth International Congress of Quantum Chemistry Satellite Symposium: Molecular Properties \u201909 \u2013 Bridging the Gap between Theory and Experiment (MP09), Oslo (Norway), Jun 2009<\/b>. (poster)<\/li>\n
      4. J. Hachmann, The Electronic Structure of 3d-M(smif)2<\/sub>: A Testing Ground for Computational Investigations of Organometallic Complexes and their Spectroscopic Characterization<\/i>, 92nd Canadian Chemistry Conference and Exhibition (CSC 2009), PTCC Division Poster Session, Hamilton (ON), Jun 2009<\/b>. (poster)<\/li>\n
      5. J. Hachmann, Application of Ab Initio DMRG to the Physics of Conjugated π-Electron Systems<\/i>, 8th Triennial Congress of the World Association of Theoretical and Computational Chemists 2008 (WATOC 2008), Sydney (Australia), Sep 2008<\/b>. (poster)<\/li>\n
      6. J. Hachmann, Ab Initio DMRG Methodology and Studies of Organic Electronic Materials<\/i>, 236th ACS National Meeting, PHYS Division Poster Session, Philadelphia (PA), Aug 2008<\/b>. (poster)<\/li>\n
      7. J. Hachmann, Recent Progress in Ab Initio DMRG Methodology<\/i>, APS March Meeting 2008, DCOMP\/DCP Symposium on Frontiers in Electronic Structure Theory, New Orleans (LA), Mar 2008<\/b>. (talk)<\/li>\n
      8. J. Hachmann, Ab Initio DMRG Methodology and Studies of Organic Electronic Materials<\/i>, 48th Sanibel Symposium, St. Simons Island (GA), Feb 2008<\/b>. (poster)<\/li>\n
      9. J. Hachmann, The Hidden Radical Nature of the Acenes<\/i>, 234th ACS National Meeting, PHYS Division Poster Session, Boston (MA), Sep 2007<\/b>. (poster)<\/li>\n
      10. J. Hachmann, The Radical Character of the Acenes<\/i>, Molecular Quantum Mechanics: Analytic Gradients and Beyond – An International Conference in Honor of Professor Peter Pulay (MQM 2007), Budapest (Hungary), Jun 2007<\/b>. (poster)<\/li>\n
      11. J. Hachmann, On the Nature of the Oligoacene Ground State<\/i>, International Conference on Strongly Correlated Electron Systems (SCES\u201907), Houston (TX), May 2007<\/b>. (poster)<\/li>\n
      12. J. Hachmann, On the Nature of the Oligoacene Ground State<\/i>, APS March Meeting 2007, Denver (CO), Mar 2007<\/b>. (poster)<\/li>\n
      13. J. Hachmann, Quadratic Scaling Ab Initio DMRG for Strong Nondynamic Correlation<\/i>, APS March Meeting 2007, DCOMP Symposium on Computational Methods for Strongly Correlated Systems and Many Body Theory, Denver (CO), Mar 2007<\/b>. (talk)<\/li>\n
      14. J. Hachmann, Development of DMRG Methodology in Electronic Structure Theory<\/i>, Cornell University MSc Project Defense Seminar, Ithaca (NY), Nov 2006<\/b>. (talk)<\/li>\n
      15. J. Hachmann, Quadratic Scaling Ab Initio DMRG for Strong Nondynamic Correlation<\/i>, 232nd ACS National Meeting, COMP Division Symposium on Quantum Chemistry, San Francisco (CA), Sep 2006<\/b>. (talk)<\/li>\n
      16. J. Hachmann, Quadratic Scaling Ab Initio DMRG for Strong Nondynamic Correlation<\/i>, 42nd Symposium on Theoretical Chemistry (STC 42): Quantum Chemistry \u2212 Methods and Applications, Erkner\/Berlin (Germany), Sep 2006<\/b>. (poster)<\/li>\n
      17. J. Hachmann, Quadratic Scaling Ab Initio DMRG for Strong Multireference Correlation<\/i>, XIIth International Congress of Quantum Chemistry (XII-ICQC2006), Kyoto (Japan), May 2006<\/b>. (poster)<\/li>\n
      18. J. Hachmann, Quadratic Scaling Ab Initio DMRG for Strong Multireference Correlation<\/i>, XIIth International Congress of Quantum Chemistry Satellite Meeting: Chemical Accuracy and Beyond \u2013 Electron Correlation, DFT, and Breakdown of Born-Oppenheimer Scheme, Tokyo (Japan), May 2006<\/b>. (poster)<\/li>\n<\/ol>\n
        \n

          <\/p>\n

        Cambridge & Jena (2003–2004)<\/h4>\n
          \n
        1. J. Hachmann, Nodal Hypersurfaces in Many-Electron Wavefunctions<\/i>, Molecular Quantum Mechanics: The No Nonsense Path to Progress – An International Conference in Honour of Professor Nicholas C. Handy (MQM 2004), Cambridge (UK), Jul 2004<\/b>. (poster)<\/li>\n
        2. J. Hachmann, Knotenhyperfl\u00e4chen in Vielelektronen-Wellenfunktionen<\/i>, University of Jena DiplChem Thesis Defense Seminar, Jena (Germany), Jul 2004<\/b>. (talk)<\/li>\n
        3. J. Hachmann, Nodal Hypersurfaces in Many-Electron Wavefunctions<\/i>, GDCh-JCF Spring Symposium, Heidelberg (Germany), Apr 2004<\/b>. (talk)<\/li>\n
        4. J. Hachmann, Nodal Hypersurfaces in Many-Electron Wavefunctions<\/i>, GDCh-JCF Spring Symposium, Heidelberg (Germany), Apr 2004<\/b>. (poster)<\/li>\n<\/ol>\n
          \n(Last update: 2022-05-11)<\/p>\n","protected":false},"excerpt":{"rendered":"

          Research Presentations Here is a listing of all the presentations, talks, and seminars (other than group meetings) given by members of the group: J. Hachmann, A Machine Learning Shortcut to Physics-Based Modeling and Simulations, Department Seminar, Department of Chemical Engineering, University of Arkansas, Fayetteville (AR), TBD 2022. (invited talk) J. Hachmann, TBD, Theory and Applications of Computational Chemistry 2020, Sapporo (Japan), TBD 2022. (invited talk) J. Hachmann, TBD, Workshop on Application of Machine Learning Algorithms to the Synthesis of Zeolites, Houston (TX), TBD 2022. (invited talk) J. Hachmann, TBD, Physical Chemistry Seminar, University of Toronto, Toronto (ON), TBD 2022. (invited talk) J. Hachmann, A Machine Learning Shortcut to Physics-Based Modeling and Simulations, Department Seminar, Department of Chemical and Biological Engineering, University of Wisconsin, Madison (WI), TBD 2022. (invited talk) J. Hachmann, The Machine Learning Route to Accelerated Discovery and Inverse Design of Materials Systems, 2022 TechConnect World Innovation Conference, Symposium on AI for Advanced Materials Design, National Harbor (MD), June 2022. (invited talk) J. Hachmann, From Physics-Based to Data-Derived Models \u2013 and Back, Center for Nonlinear Studies Annual Conference 2022 – Physics Informed Machine Learning, Santa Fe (NM), May 2022. (invited talk) A. Sonpal, Developing eXplainable Artificial Intelligence (XAI) Methods for the Molecular Sciences, 2022 UB CBE Graduate Student Seminar, Buffalo (NY), May 2022. (invited talk) G. Vishwakarma, Adapting Evolutionary Algorithms for Data-driven Discovery of Liquid Organic Hydrogen Carriers, 263rd ACS National Meeting, CINF Division Symposium on Chemical Information Across the Chemistry Enterprise, San Diego (CA), Mar 2022. (poster) G. Vishwakarma, Evaluating Homogeneous Catalysts for Hydrogen Extraction from Liquid Organic Hydrogen Carriers, 263rd ACS National Meeting, ENFL Division Symposium on Energy Storage in Chemical Bonds: From Theory to Practice, San Diego (CA), Mar 2022. (talk) J. Hachmann, Making Machine Learning Work in Chemical and Materials Research, Department Seminar, Department of Materials Science and Engineering, Pennsylvania State University, virtual, Dec 2021. (invited talk) G. Vishwakarma, Acceptorless Catalytic Dehydrogenation of Liquid Organic Hydrogen Carriers, 24th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2021. (poster) A. Sonpal, Benchmarking Methods to Explain the Predictions of Black-Box Deep Neural Networks, 24th Annual UB CBE Graduate Student Research Symposium, Buffalo (NY), Oct 2021. (poster) J. Hachmann, The Machine Learning Route to Accelerated Discovery and Inverse Design of Chemical and Materials Systems, 2021 AIChE Annual Meeting, CoMSEF Symposium on Data-Driven Design and Modeling, Boston (MA), Nov 2021. (invited talk) J. Hachmann, Tailoring Machine Learning for the Chemistry Domain, Telluride Workshop on Machine Learning and Informatics for Chemistry and Materials, Telluride (CO), Sep 2021. (invited talk) N.M. Murthy, Synthetic Accessibility Scoring and its Potential Applications in Chemical Library Generation, University at Buffalo MSc Thesis Defense Seminar, virtual, Aug 2021. (talk) D.N. Chheda, Machine Learning Classifiers for the Performance Prediction of High Refractive Index Polyimides, University at Buffalo MSc Thesis Defense Seminar, virtual, May 2021. (talk) Y. Pal, Improved Energy Conversion Materials from Combined Data-Driven and Computational Screenings, University at Buffalo PhD Dissertation Defense Seminar, virtual, May 2021. (talk) J. Hachmann, Advancing Computational Chemistry with Machine Learning: From Physics-Based to Data-Derived Models and Back, Physical and Theoretical Chemistry Seminar, University of Graz (Austria), virtual, May 2021. (invited talk) Y. Pal, Materials Discovery Towards Cheaper Fuel Cell Vehicles, 23rd Annual UB CBE Graduate Student Research Symposium, virtual, Nov 2020. (poster) G. Vishwakarma, A Virtual High-throughput Study for Traversing Energy Barriers in Regenerative Energy Systems, 23rd Annual UB CBE Graduate Student Research Symposium, virtual, Nov 2020. (poster) A. Sonpal, Developing XAI Methodology for the Chemical Sciences, 23rd Annual UB CBE Graduate Student Research Symposium, virtual, Nov 2020. (poster) A. Pradhan, Tailor-Made Materials – Inverse Engineering Compounds using Feature Correlation, 23rd Annual UB CBE Graduate Student Research Symposium, virtual, Nov 2020. (poster) J. Hachmann, Advancing Computational Chemistry with Machine Learning: From Physics-Based to Data-Derived Models and Back, Computational Chemistry Network Group Seminar, Pfizer, virtual, Oct 2020. (invited talk) J. Hachmann, From High-Throughput Computational Chemistry and Molecular Pattern Recognition to the Targeted Design of Novel Chemistry, 260th ACS National Meeting, CINF Division Symposium on From Bench to Market: Leveraging AI & Advanced Computational Methods, virtual, Aug 2020. (invited talk) J. Hachmann, Advancing Computational Chemistry with Machine Learning: From Physics-Based to Data-Derived Models and Back, 260th ACS National Meeting, COMP Division OpenEye Outstanding Junior Faculty Award Session, virtual, Aug 2020. (poster) A. Pradhan, Tailor-Made Materials: Inverse Engineering Compounds Using Feature Correlation, University at Buffalo MSc Thesis Defense Seminar, virtual, July 2020. (talk) K. Pratidar, Predicting Spin Symmetry Breaking in Organic Photovoltaic Compounds Using Data Mining, University at Buffalo MSc Thesis Defense Seminar, virtual, May 2020. (talk) J. Hachmann, A Machine Learning Shortcut to Physics-Based Molecular Modeling, Department Seminar, Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh (PA), Mar 2020. (invited talk) J. Hachmann, The Machine Learning Route to Accelerated Discovery and Inverse Design, NIST Materials Genome Initiative Seminar, National Institute of Standards and Technology, Gaithersburg (MD), Feb 2020. (invited talk) J. Hachmann, Making Machine Learning Work in Chemical and Materials Research, Site Seminar, US Army Research Laboratory, Aberdeen Proving Ground (MD), Jan 2020. (invited talk) J. Hachmann, The Machine Learning Route to Accelerated Discovery and Inverse Design, 2019 MRS Fall Meeting, Symposium on Closing the Loop – Using Machine Learning in High-Throughput Discovery of New Materials, Boston (MA), Dec 2019. (invited talk) J. Hachmann, Making Machine Learning Work in Chemistry, NSF MolSSI Workshop on Machine Learning and Chemistry: Challenges on the Way Forward, College Park (MD), Nov 2019. (invited talk) J. Hachmann, A Deep Neural Network Model for MD-Level Packing Density Predictions and Its Application in the Study of 1.5 Million Organic Molecules, 2019 AIChE Annual Meeting, CoMSEF Symposium on Practical Applications of Computational Chemistry and Molecular Simulation, Orlando (FL), Nov 2019. (talk) J. Hachmann, Advancing Machine Learning Methodology for the Chemical and Materials Domain, 2019 AIChE Annual Meeting, Topical Conference on Applications of Data Science to Molecules and Materials, Symposium on Innovations in Methods of Data Science, Orlando (FL), Nov 2019. (talk) J. Hachmann, Machine Learning for Molecular Property Predictions and the Software Ecosystem That Enables It, 2019<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":24,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-426","page","type-page","status-publish","hentry","entry","post-inner"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/pages\/426","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/comments?post=426"}],"version-history":[{"count":0,"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/pages\/426\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/pages\/24"}],"wp:attachment":[{"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/media?parent=426"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}