Publications by Topic

HTPS, Big Data, Machine Learning | Organic Electronics, Optical Polymers | Catalysis, Reactions, Coordination Chemistry | New Methods | DMRG | Other Topics | Theses, Dissertations

High-Throughput Screening, Big Data, and Machine Learning

  • M.A.F. Afzal, A. Sonpal, M. Haghighatlari, A.J. Schultz, J. Hachmann, A Deep Neural Network Model for Packing Density Predictions and its Application in the Study of 1.5 Million Organic Molecules, ChemRxiv (2019), 8217758.
    DOI: 10.26434/chemrxiv.8217758.v1
  • M.A.F. Afzal, M. Haghighatlari, S.P. Ganesh, C. Cheng, J. Hachmann, Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining, J. Phys. Chem. C 123 (2019), 14610-14618. (invited)
    DOI: 10.1021/acs.jpcc.9b01147
  • M.A.F. Afzal, J. Hachmann, High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design, in Handbook on Big Data and Machine Learning in the Physical Sciences, Vol 1: Big Data Methods in Experimental Materials Discovery, S. Kalidindi, S.V. Kalinin, T. Lookman, I. Foster (Eds.), World Scientific, Singapore (2019), accepted. (invited)
    ISBN: 978-981-120-444-9; DOI: arXiv:1902.03721
  • M. Haghighatlari, J. Hachmann, Advances of Machine Learning in Molecular Modeling and Simulation, Curr. Opin. Chem. Eng. 23 (2019), 51-57. (invited)
    DOI: 10.1016/j.coche.2019.02.009
  • M.A.F. Afzal, J. Hachmann, Benchmarking DFT Approaches for the Calculation of Polarizability Inputs for Refractive Index Predictions in Organic Polymers, Phys. Chem. Chem. Phys. 21 (2019), 4452-4460.
    DOI: 10.1039/C8CP05492D
  • A. Sonpal, J. Hachmann, Predicting Melting Points of Deep Eutectic Solvents, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: 10477/78667
  • G. Vishwakama, J. Hachmann, Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: 10477/78589
  • M.A.F. Afzal, J. Hachmann, From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications, PhD Dissertation, University at Buffalo – SUNY (2018).
    HDL: 10477/77967
  • A.L. Ferguson, J. Hachmann, Machine Learning and Data Science in Materials Design: A Themed Collection (Editorial), Mol. Syst. Des. Eng. 3 (2018), 429-430.
    DOI: 10.1039/C8ME90007H
  • J. Hachmann, T. Windus, J. McLean, V. Allwardt, A. Schrimpe-Rutledge, M.A.F. Afzal, M. Haghighatlari, Framing the Role of Big Data and Modern Data Science in Chemistry, NSF CHE Workshop Report (2018).
    DOI: TBD
  • J. Hachmann, M.A.F. Afzal, M. Haghighatlari, Y. Pal, Building and Deploying a Cyberinfrastructure for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space, Mol. Simul. 44 (2018), 921-929. (invited)
    DOI: 10.1080/08927022.2018.1471692
  • V. Kumaran Sudalayandi Rajeswari, J. Hachmann, First-Principles Modeling of Polymer Degradation Kinetics and Virtual High-Throughput Screening of Candidates for Biodegradable Polymers, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: TBD
  • M.A.F. Afzal, C. Cheng, J. Hachmann, Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers, J. Chem. Phys. 148 (2018), 241712. (invited)
    DOI: 10.1063/1.5007873
  • Y. Tian, J. Hachmann, Inheritance of Molecular Orbital Energies from Monomer Building Blocks to Larger Copolymers in Organic Semiconductors, MSc Thesis, University at Buffalo – SUNY (2016).
    HDL: 10477/76228
  • E.O. Pyzer-Knapp, G. Simm, T. Lutzow, K. Li, L.R. Seress, J. Hachmann, A. Aspuru-Guzik, The Harvard Organic Photovoltaic Dataset, Sci. Data 3 (2016), 160086.
    DOI: 10.1038/sdata.2016.86
  • C.-Y. Shih, J. Hachmann, Systematic Trends in Results from Different Density Functional Theory Models, MSc Thesis, University at Buffalo – SUNY (2015).
    HDL: 10477/51816
  • J. Hachmann, R. Olivares-Amaya, A. Jinich, A.L. Appleton, M.A. Blood-Forsythe, L.R. Seress, C. Román-Salgado, K. Trepte, S. Atahan-Evrenk, S. Er, S. Shrestha, R. Mondal, A. Sokolov, Z. Bao, A. Aspuru-Guzik, Lead Candidates for High-Performance Organic Photovoltaics from High-Throughput Quantum Chemistry – the Harvard Clean Energy Project, Energy Environ. Sci. 7 (2014), 698-704.
    DOI: 10.1039/c3ee42756k
  • C. Amador-Bedolla, R. Olivares-Amaya, J. Hachmann, A. Aspuru-Guzik, Organic Photovoltaics, in Informatics for Materials Science and Engineering – Data-driven Discovery for Accelerated Experimentation and Application, K. Rajan (Ed.), Elsevier, Amsterdam (2013), 423-442. (invited)
    DOI: 10.1016/B978-0-12-394399-6.00017-5
  • R. Olivares-Amaya, C. Amador-Bedolla, J. Hachmann, S. Atahan-Evrenk, R.S. Sánchez-Carrera, L. Vogt, A. Aspuru-Guzik, Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics, Energy Environ. Sci. 4 (2011), 4849-4861.
    DOI: 10.1039/c1ee02056k
  • J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, C. Amador-Bedolla, R.S. Sánchez-Carrera, A. Gold-Parker, L. Vogt, A.M. Brockway, A. Aspuru-Guzik, The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid, J. Phys. Chem. Lett. 2 (2011), 2241-2251. (invited)
    DOI: 10.1021/jz200866s

 

Organic Electronics and Optical Polymers

  • M.A.F. Afzal, M. Haghighatlari, S.P. Ganesh, C. Cheng, J. Hachmann, Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining, J. Phys. Chem. C 123 (2019), 14610-14618. (invited)
    DOI: 10.1021/acs.jpcc.9b01147
  • M.A.F. Afzal, J. Hachmann, Benchmarking DFT Approaches for the Calculation of Polarizability Inputs for Refractive Index Predictions in Organic Polymers, Phys. Chem. Chem. Phys. 21 (2019), 4452-4460.
    DOI: 10.1039/C8CP05492D
  • G. Vishwakama, J. Hachmann, Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: 10477/78589
  • M.A.F. Afzal, J. Hachmann, From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications, PhD Dissertation, University at Buffalo – SUNY (2018).
    HDL: 10477/77967
  • M.A.F. Afzal, C. Cheng, J. Hachmann, Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers, J. Chem. Phys. 148 (2018), 241712. (invited)
    DOI: 10.1063/1.5007873
  • Y. Tian, J. Hachmann, Inheritance of Molecular Orbital Energies from Monomer Building Blocks to Larger Copolymers in Organic Semiconductors, MSc Thesis, University at Buffalo – SUNY (2016).
    HDL: 10477/76228
  • E.O. Pyzer-Knapp, G. Simm, T. Lutzow, K. Li, L.R. Seress, J. Hachmann, A. Aspuru-Guzik, The Harvard Organic Photovoltaic Dataset, Sci. Data 3 (2016), 160086.
    DOI: 10.1038/sdata.2016.86
  • C.-Y. Shih, J. Hachmann, Systematic Trends in Results from Different Density Functional Theory Models, MSc Thesis, University at Buffalo – SUNY (2015).
    HDL: 10477/51816
  • J. Hachmann, R. Olivares-Amaya, A. Jinich, A.L. Appleton, M.A. Blood-Forsythe, L.R. Seress, C. Román-Salgado, K. Trepte, S. Atahan-Evrenk, S. Er, S. Shrestha, R. Mondal, A. Sokolov, Z. Bao, A. Aspuru-Guzik, Lead Candidates for High-Performance Organic Photovoltaics from High-Throughput Quantum Chemistry – the Harvard Clean Energy Project, Energy Environ. Sci. 7 (2014), 698-704.
    DOI: 10.1039/c3ee42756k
  • C. Amador-Bedolla, R. Olivares-Amaya, J. Hachmann, A. Aspuru-Guzik, Organic Photovoltaics, in Informatics for Materials Science and Engineering – Data-driven Discovery for Accelerated Experimentation and Application, K. Rajan (Ed.), Elsevier, Amsterdam (2013), 423-442. (invited)
    DOI: 10.1016/B978-0-12-394399-6.00017-5
  • R. Olivares-Amaya, C. Amador-Bedolla, J. Hachmann, S. Atahan-Evrenk, R.S. Sánchez-Carrera, L. Vogt, A. Aspuru-Guzik, Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics, Energy Environ. Sci. 4 (2011), 4849-4861.
    DOI: 10.1039/c1ee02056k
  • J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, C. Amador-Bedolla, R.S. Sánchez-Carrera, A. Gold-Parker, L. Vogt, A.M. Brockway, A. Aspuru-Guzik, The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid, J. Phys. Chem. Lett. 2 (2011), 2241-2251. (invited)
    DOI: 10.1021/jz200866s
  • J. Hachmann, G.K.-L. Chan, Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry, PhD Dissertation, Cornell University (2010).
    HDL: 1813/14774
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Analytic Response Theory for the Density Matrix Renormalization Group, J. Chem. Phys. 130 (2009), 184111.
    DOI: 10.1063/1.3121422
  • D. Ghosh, J. Hachmann, T. Yanai, G.K.-L. Chan, Orbital Optimization in the Density Matrix Renormalization Group, with Application to Polyenes and β-Carotene, J. Chem. Phys. 128 (2008), 144117.
    DOI: 10.1063/1.2883976
  • J. Hachmann, J.J. Dorando, M. Avilés, G.K.-L. Chan, The Radical Character of the Acenes: A Density Matrix Renormalization Group Study, J. Chem. Phys. 127 (2007), 134309.
    DOI: 10.1063/1.2768362
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Targeted Excited State Algorithms, J. Chem. Phys. 127 (2007), 084109.
    DOI: 10.1063/1.2768360
  • J. Hachmann, W. Cardoen, G.K.-L. Chan, Multireference Correlation in Long Molecules with the Quadratic Scaling Density Matrix Renormalization Group, J. Chem. Phys. 125 (2006), 144101.
    DOI: 10.1063/1.2345196

 

Catalysis, Reactions, and Coordination Chemistry

  • M.A.F. Afzal, J. Hachmann, High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design, in Handbook on Big Data and Machine Learning in the Physical Sciences, Vol 1: Big Data Methods in Experimental Materials Discovery, S. Kalidindi, S.V. Kalinin, T. Lookman, I. Foster (Eds.), World Scientific, Singapore (2019), accepted. (invited)
    ISBN: 978-981-120-444-9; DOI: arXiv:1902.03721
  • R. Asatryan, Y. Pal, J. Hachmann, E. Ruckenstein, Roaming-Like Mechanism for the Dehydration of Diol Radicals, J. Phys. Chem. A 122 (2018), 9738-9754.
    DOI: 10.1021/acs.jpca.8b08690
  • V. Kumaran Sudalayandi Rajeswari, J. Hachmann, First-Principles Modeling of Polymer Degradation Kinetics and Virtual High-Throughput Screening of Candidates for Biodegradable Polymers, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: TBD
  • R. Asatryan, E. Ruckenstein, J. Hachmann, Revisiting the Polytopal Rearrangements in Penta-Coordinate d7-Metallocomplexes: Modified Berry Pseudorotation, Octahedral Switch, and Butterfly Isomerization, Chem. Sci. 8 (2017), 5512-5525.
    DOI: 10.1039/c7sc00703e
  • J. Hachmann, B.A. Frazier, P.T. Wolczanski, G.K.-L. Chan, A Theoretical Study of the 3d-M(smif)2 Complexes: Structure, Magnetism, and Oxidation States, ChemPhysChem 12 (2011), 3236-3244.
    DOI: 10.1002/cphc.201100286
  • J. Hachmann, G.K.-L. Chan, Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry, PhD Dissertation, Cornell University (2010).
    HDL: 1813/14774

 

New Methods

  • M.A.F. Afzal, A. Sonpal, M. Haghighatlari, A.J. Schultz, J. Hachmann, A Deep Neural Network Model for Packing Density Predictions and its Application in the Study of 1.5 Million Organic Molecules, ChemRxiv (2019), 8217758.
    DOI: 10.26434/chemrxiv.8217758.v1
  • M. Haghighatlari, J. Hachmann, Advances of Machine Learning in Molecular Modeling and Simulation, Curr. Opin. Chem. Eng. 23 (2019), 51-57. (invited)
    DOI: 10.1016/j.coche.2019.02.009
  • G. Vishwakama, J. Hachmann, Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: 10477/78589
  • J. Hachmann, M.A.F. Afzal, M. Haghighatlari, Y. Pal, Building and Deploying a Cyberinfrastructure for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space, Mol. Simul. 44 (2018), 921-929. (invited)
    DOI: 10.1080/08927022.2018.1471692
  • J. Hachmann, G.K.-L. Chan, Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry, PhD Dissertation, Cornell University (2010).
    HDL: 1813/14774
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Analytic Response Theory for the Density Matrix Renormalization Group, J. Chem. Phys. 130 (2009), 184111.
    DOI: 10.1063/1.3121422
  • D. Ghosh, J. Hachmann, T. Yanai, G.K.-L. Chan, Orbital Optimization in the Density Matrix Renormalization Group, with Application to Polyenes and β-Carotene, J. Chem. Phys. 128 (2008), 144117.
    DOI: 10.1063/1.2883976
  • G.K.-L. Chan, J.J. Dorando, D. Ghosh, J. Hachmann, E. Neuscamman, H. Wang, T. Yanai, An Introduction to the Density Matrix Renormalization Group Ansatz in Quantum Chemistry, Prog. Theor. Chem. Phys. 18 (2008), 49-65.
    DOI: 10.1007/978-1-4020-8707-3_4
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Targeted Excited State Algorithms, J. Chem. Phys. 127 (2007), 084109.
    DOI: 10.1063/1.2768360
  • J. Hachmann, W. Cardoen, G.K.-L. Chan, Multireference Correlation in Long Molecules with the Quadratic Scaling Density Matrix Renormalization Group, J. Chem. Phys. 125 (2006), 144101.
    DOI: 10.1063/1.2345196

 

DMRG

  • J. Hachmann, G.K.-L. Chan, Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry, PhD Dissertation, Cornell University (2010).
    HDL: 1813/14774
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Analytic Response Theory for the Density Matrix Renormalization Group, J. Chem. Phys. 130 (2009), 184111.
    DOI: 10.1063/1.3121422
  • D. Ghosh, J. Hachmann, T. Yanai, G.K.-L. Chan, Orbital Optimization in the Density Matrix Renormalization Group, with Application to Polyenes and β-Carotene, J. Chem. Phys. 128 (2008), 144117.
    DOI: 10.1063/1.2883976
  • G.K.-L. Chan, J.J. Dorando, D. Ghosh, J. Hachmann, E. Neuscamman, H. Wang, T. Yanai, An Introduction to the Density Matrix Renormalization Group Ansatz in Quantum Chemistry, Prog. Theor. Chem. Phys. 18 (2008), 49-65.
    DOI: 10.1007/978-1-4020-8707-3_4
  • J. Hachmann, J.J. Dorando, M. Avilés, G.K.-L. Chan, The Radical Character of the Acenes: A Density Matrix Renormalization Group Study, J. Chem. Phys. 127 (2007), 134309.
    DOI: 10.1063/1.2768362
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Targeted Excited State Algorithms, J. Chem. Phys. 127 (2007), 084109.
    DOI: 10.1063/1.2768360
  • J. Hachmann, W. Cardoen, G.K.-L. Chan, Multireference Correlation in Long Molecules with the Quadratic Scaling Density Matrix Renormalization Group, J. Chem. Phys. 125 (2006), 144101.
    DOI: 10.1063/1.2345196

 

Other Topics

  • K. Mukherjee, J. Hachmann, Computational Modeling of Carboxylic-Based Organic Molecules for Li-Ion Battery Anode Materials, MSc Thesis, University at Buffalo – SUNY (2019).
    HDL: TBD
  • J. Hachmann, N.C. Handy, Nodal Hypersurfaces and Sign Domains in Many-Electron Wavefunctions, DiplChem Thesis, University of Jena (2004).
    HDL: N/A
  • J. Hachmann, P.T.A. Galek, T. Yanai, G.K.-L. Chan, N.C. Handy, The Nodes of Hartree-Fock Wavefunctions and their Orbitals, Chem. Phys. Lett. 392 (2004), 55-61.
    DOI: 10.1016/j.cplett.2004.04.070

 

Theses and Dissertations

  • K. Mukherjee, J. Hachmann, Computational Modeling of Carboxylic-Based Organic Molecules for Li-Ion Battery Anode Materials, MSc Thesis, University at Buffalo – SUNY (2019).
    HDL: TBD
  • A. Sonpal, J. Hachmann, Predicting Melting Points of Deep Eutectic Solvents, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: 10477/78667
  • G. Vishwakama, J. Hachmann, Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: 10477/78589
  • M.A.F. Afzal, J. Hachmann, From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications, PhD Dissertation, University at Buffalo – SUNY (2018).
    HDL: 10477/77967
  • V. Kumaran Sudalayandi Rajeswari, J. Hachmann, First-Principles Modeling of Polymer Degradation Kinetics and Virtual High-Throughput Screening of Candidates for Biodegradable Polymers, MSc Thesis, University at Buffalo – SUNY (2018).
    HDL: TBD
  • Y. Tian, J. Hachmann, Inheritance of Molecular Orbital Energies from Monomer Building Blocks to Larger Copolymers in Organic Semiconductors, MSc Thesis, University at Buffalo – SUNY (2016).
    HDL: 10477/76228
  • C.-Y. Shih, J. Hachmann, Systematic Trends in Results from Different Density Functional Theory Models, MSc Thesis, University at Buffalo – SUNY (2015).
    HDL: 10477/51816
  • J. Hachmann, G.K.-L. Chan, Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry, PhD Dissertation, Cornell University (2010).
    HDL: 1813/14774
  • J. Hachmann, N.C. Handy, Nodal Hypersurfaces and Sign Domains in Many-Electron Wavefunctions, DiplChem Thesis, University of Jena (2004).
    HDL: N/A
(Last update: 2019-06-05)