{"id":428,"date":"2013-08-08T23:42:56","date_gmt":"2013-08-09T04:42:56","guid":{"rendered":"http:\/\/hachmannlab.cbe.buffalo.edu\/?page_id=428"},"modified":"2019-06-17T15:15:42","modified_gmt":"2019-06-17T20:15:42","slug":"publications-by-topic","status":"publish","type":"page","link":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/publications\/publications-by-topic\/","title":{"rendered":"Publications by Topic"},"content":{"rendered":"<h3>Publications by Topic<\/h3>\n<p>\n<a href=\"#htps\">HTPS, Big Data, Machine Learning<\/a> | <a href=\"#organic_electronics\">Organic Electronics, Optical Polymers<\/a> | <a href=\"#catalysis\">Catalysis, Reactions, Coordination Chemistry<\/a> | <a href=\"#new_methods\">New Methods<\/a> | <a href=\"#dmrg\">DMRG<\/a> | <a href=\"#other\">Other Topics<\/a> | <a href=\"#theses\">Theses, Dissertations<\/a>\n<\/p>\n<hr class=\"style-joh1\" \/>\n<p><a id=\"htps\"><\/p>\n<h4>High-Throughput Screening, Big Data, and Machine Learning<\/h4>\n<p><\/a> <\/p>\n<ul style=\"text-align: justify;\">\n<li value=\"33\">M.A.F. Afzal, A. Sonpal, M. Haghighatlari, A.J. Schultz, J. Hachmann, <i>A Deep Neural Network Model for Packing Density Predictions and its Application in the Study of 1.5 Million Organic Molecules<\/i>, ChemRxiv (<b>2019<\/b>), 8217758. <br \/>DOI: <a href=\"https:\/\/doi.org\/10.26434\/chemrxiv.8217758.v1\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.26434\/chemrxiv.8217758.v1<\/a> <\/li>\n<li value=\"31\">M.A.F. Afzal, M. Haghighatlari, S.P. Ganesh, C. Cheng, J. Hachmann, <i>Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining<\/i>, J. Phys. Chem. C 123 (<b>2019<\/b>), 14610-14618. (invited) <br \/>DOI: <a href=\"https:\/\/doi.org\/10.1021\/acs.jpcc.9b01147\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1021\/acs.jpcc.9b01147<\/a> <\/li>\n<li value=\"30\">M.A.F. Afzal, J. Hachmann, <i>High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design<\/i>, 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 (<b>2019<\/b>), accepted. (invited) <br \/> ISBN: 978-981-120-444-9; DOI: <a href=\"https:\/\/arxiv.org\/abs\/1902.03721\" target=\"_blank\" rel=\"noopener noreferrer\"> arXiv:1902.03721<\/a> <\/li>\n<li value=\"29\">M. Haghighatlari, J. Hachmann, <i>Advances of Machine Learning in Molecular Modeling and Simulation<\/i>, Curr. Opin. Chem. Eng. 23 (<b>2019<\/b>), 51-57. (invited) <br \/>DOI: <a href=\"https:\/\/doi.org\/10.1016\/j.coche.2019.02.009\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1016\/j.coche.2019.02.009<\/a> <\/li>\n<li value=\"28\">M.A.F. Afzal, J. Hachmann, <i>Benchmarking DFT Approaches for the Calculation of Polarizability Inputs for Refractive Index Predictions in Organic Polymers<\/i>, Phys. Chem. Chem. Phys. 21 (<b>2019<\/b>), 4452-4460. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1039\/C8CP05492D\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1039\/C8CP05492D<\/a> <\/li>\n<li value=\"26\">A. Sonpal, J. Hachmann, <i>Predicting Melting Points of Deep Eutectic Solvents<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/78667\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/78667<\/a> <\/li>\n<li value=\"25\">G. Vishwakama, J. Hachmann, <i>Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/78589\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/78589<\/a> <\/li>\n<li value=\"24\">M.A.F. Afzal, J. Hachmann, <i>From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications<\/i>, PhD Dissertation, University at Buffalo \u2013 SUNY (<b>2018<\/b>).<br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/77967\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/77967<\/a> <\/li>\n<li value=\"23\"> A.L. Ferguson, J. Hachmann, <i>Machine Learning and Data Science in Materials Design: A Themed Collection<\/i> (Editorial), Mol. Syst. Des. Eng. 3 (<b>2018<\/b>), 429-430. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1039\/C8ME90007H\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1039\/C8ME90007H<\/a><\/li>\n<li value=\"22\"> J. Hachmann, T. Windus, J. McLean, V. Allwardt, A. Schrimpe-Rutledge, M.A.F. Afzal, M. Haghighatlari, <i>Framing the Role of Big Data and Modern Data Science in Chemistry<\/i>, NSF CHE Workshop Report (<b>2018<\/b>). <br \/> DOI: TBD<\/li>\n<li value=\"21\">J. Hachmann, M.A.F. Afzal, M. Haghighatlari, Y. Pal, <i>Building and Deploying a Cyberinfrastructure for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space<\/i>, Mol. Simul. 44 (<b>2018<\/b>), 921-929. (invited) <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1080\/08927022.2018.1471692\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1080\/08927022.2018.1471692<\/a><\/li>\n<li value=\"20\">V. Kumaran Sudalayandi Rajeswari, J. Hachmann, <i>First-Principles Modeling of Polymer Degradation Kinetics and Virtual High-Throughput Screening of Candidates for Biodegradable Polymers<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>).<br \/> HDL: TBD <\/li>\n<li value=\"19\">M.A.F. Afzal, C. Cheng, J. Hachmann, <i>Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers<\/i>, J. Chem. Phys. 148 (<b>2018<\/b>), 241712.  (invited) <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.5007873\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.5007873<\/a><\/li>\n<li value=\"17\">Y. Tian, J. Hachmann, <i>Inheritance of Molecular Orbital Energies from Monomer Building Blocks to Larger Copolymers in Organic Semiconductors<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2016<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/76228\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/76228<\/a> <\/li>\n<li value=\"16\">E.O. Pyzer-Knapp, G. Simm, T. Lutzow, K. Li, L.R. Seress, J. Hachmann, A. Aspuru-Guzik, <i>The Harvard Organic Photovoltaic Dataset<\/i>, Sci. Data 3 (<b>2016<\/b>), 160086. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1038\/sdata.2016.86\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1038\/sdata.2016.86<\/a><\/li>\n<li value=\"15\">C.-Y. Shih, J. Hachmann, <i>Systematic Trends in Results from Different Density Functional Theory Models<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2015<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/51816\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/51816<\/a> <\/li>\n<li value=\"14\">J. Hachmann, R. Olivares-Amaya, A. Jinich, A.L. Appleton, M.A. Blood-Forsythe, L.R. Seress, C. Rom\u00e1n-Salgado, K. Trepte, S. Atahan-Evrenk, S. Er, S. Shrestha, R. Mondal, A. Sokolov, Z. Bao, A. Aspuru-Guzik, <i>Lead Candidates for High-Performance Organic Photovoltaics from High-Throughput Quantum Chemistry &ndash; the Harvard Clean Energy Project<\/i>, Energy Environ. Sci. 7 (<b>2014<\/b>), 698-704. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1039\/c3ee42756k\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1039\/c3ee42756k<\/a><\/li>\n<li value=\"13\">C. Amador-Bedolla, R. Olivares-Amaya, J. Hachmann, A. Aspuru-Guzik, <i>Organic Photovoltaics<\/i>, in Informatics for Materials Science and Engineering &ndash; Data-driven Discovery for Accelerated Experimentation and Application, K. Rajan (Ed.), Elsevier, Amsterdam (<b>2013<\/b>), 423-442. (invited) <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1016\/B978-0-12-394399-6.00017-5\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1016\/B978-0-12-394399-6.00017-5<\/a><\/li>\n<li value=\"12\">R. Olivares-Amaya, C. Amador-Bedolla, J. Hachmann, S. Atahan-Evrenk, R.S. S\u00e1nchez-Carrera, L. Vogt, A. Aspuru-Guzik, <i>Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics<\/i>, Energy Environ. Sci. 4 (<b>2011<\/b>), 4849-4861. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1039\/c1ee02056k\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1039\/c1ee02056k<\/a><\/li>\n<li value=\"11\">J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, C. Amador-Bedolla, R.S. S\u00e1nchez-Carrera, A. Gold-Parker, L. Vogt, A.M. Brockway, A. Aspuru-Guzik, <i>The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid<\/i>, J. Phys. Chem. Lett. 2 (<b>2011<\/b>), 2241-2251. (invited) <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1021\/jz200866s\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1021\/jz200866s<\/a> <\/li>\n<\/ul>\n<hr class=\"style-joh1\" \/>\n<p> &nbsp; <\/p>\n<p><a id=\"organic_electronics\"><\/p>\n<h4>Organic Electronics and Optical Polymers<\/h4>\n<p><\/a> <\/p>\n<ul style=\"text-align: justify;\">\n<li value=\"31\">M.A.F. Afzal, M. Haghighatlari, S.P. Ganesh, C. Cheng, J. Hachmann, <i>Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining<\/i>, J. Phys. Chem. C 123 (<b>2019<\/b>), 14610-14618. (invited) <br \/>DOI: <a href=\"https:\/\/doi.org\/10.1021\/acs.jpcc.9b01147\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1021\/acs.jpcc.9b01147<\/a> <\/li>\n<li value=\"28\">M.A.F. Afzal, J. Hachmann, <i>Benchmarking DFT Approaches for the Calculation of Polarizability Inputs for Refractive Index Predictions in Organic Polymers<\/i>, Phys. Chem. Chem. Phys. 21 (<b>2019<\/b>), 4452-4460. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1039\/C8CP05492D\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1039\/C8CP05492D<\/a> <\/li>\n<li value=\"25\">G. Vishwakama, J. Hachmann, <i>Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/78589\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/78589<\/a> <\/li>\n<li value=\"24\">M.A.F. Afzal, J. Hachmann, <i>From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications<\/i>, PhD Dissertation, University at Buffalo \u2013 SUNY (<b>2018<\/b>).<br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/77967\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/77967<\/a> <\/li>\n<li value=\"19\">M.A.F. Afzal, C. Cheng, J. Hachmann, <i>Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers<\/i>, J. Chem. Phys. 148 (<b>2018<\/b>), 241712.  (invited) <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.5007873\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.5007873<\/a><\/li>\n<li value=\"17\">Y. Tian, J. Hachmann, <i>Inheritance of Molecular Orbital Energies from Monomer Building Blocks to Larger Copolymers in Organic Semiconductors<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2016<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/76228\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/76228<\/a> <\/li>\n<li value=\"16\">E.O. Pyzer-Knapp, G. Simm, T. Lutzow, K. Li, L.R. Seress, J. Hachmann, A. Aspuru-Guzik, <i>The Harvard Organic Photovoltaic Dataset<\/i>, Sci. Data 3 (<b>2016<\/b>), 160086. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1038\/sdata.2016.86\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1038\/sdata.2016.86<\/a><\/li>\n<li value=\"15\">C.-Y. Shih, J. Hachmann, <i>Systematic Trends in Results from Different Density Functional Theory Models<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2015<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/51816\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/51816<\/a> <\/li>\n<li value=\"14\">J. Hachmann, R. Olivares-Amaya, A. Jinich, A.L. Appleton, M.A. Blood-Forsythe, L.R. Seress, C. Rom\u00e1n-Salgado, K. Trepte, S. Atahan-Evrenk, S. Er, S. Shrestha, R. Mondal, A. Sokolov, Z. Bao, A. Aspuru-Guzik, <i>Lead Candidates for High-Performance Organic Photovoltaics from High-Throughput Quantum Chemistry &ndash; the Harvard Clean Energy Project<\/i>, Energy Environ. Sci. 7 (<b>2014<\/b>), 698-704. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1039\/c3ee42756k\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1039\/c3ee42756k<\/a><\/li>\n<li value=\"13\">C. Amador-Bedolla, R. Olivares-Amaya, J. Hachmann, A. Aspuru-Guzik, <i>Organic Photovoltaics<\/i>, in Informatics for Materials Science and Engineering &ndash; Data-driven Discovery for Accelerated Experimentation and Application, K. Rajan (Ed.), Elsevier, Amsterdam (<b>2013<\/b>), 423-442. (invited) <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1016\/B978-0-12-394399-6.00017-5\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1016\/B978-0-12-394399-6.00017-5<\/a><\/li>\n<li value=\"12\">R. Olivares-Amaya, C. Amador-Bedolla, J. Hachmann, S. Atahan-Evrenk, R.S. S\u00e1nchez-Carrera, L. Vogt, A. Aspuru-Guzik, <i>Accelerated Computational Discovery of High-Performance Materials for Organic Photovoltaics by Means of Cheminformatics<\/i>, Energy Environ. Sci. 4 (<b>2011<\/b>), 4849-4861. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1039\/c1ee02056k\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1039\/c1ee02056k<\/a><\/li>\n<li value=\"11\">J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, C. Amador-Bedolla, R.S. S\u00e1nchez-Carrera, A. Gold-Parker, L. Vogt, A.M. Brockway, A. Aspuru-Guzik, <i>The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid<\/i>, J. Phys. Chem. Lett. 2 (<b>2011<\/b>), 2241-2251. (invited) <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1021\/jz200866s\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1021\/jz200866s<\/a> <\/li>\n<li value=\"9\">J. Hachmann, G.K.-L. Chan, <i>Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry<\/i>, PhD Dissertation, Cornell University (<b>2010<\/b>).<br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/1813\/14774\" target=\"_blank\" rel=\"noopener noreferrer\"> 1813\/14774<\/a> <\/li>\n<li value=\"8\">J.J. Dorando, J. Hachmann, G.K.-L. Chan, <i>Analytic Response Theory for the Density Matrix Renormalization Group<\/i>, J. Chem. Phys. 130 (<b>2009<\/b>), 184111. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.3121422\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.3121422<\/a><\/li>\n<li value=\"7\">D. Ghosh, J. Hachmann, T. Yanai, G.K.-L. Chan, <i>Orbital Optimization in the Density Matrix Renormalization Group, with Application to Polyenes and &beta;-Carotene<\/i>, J. Chem. Phys. 128 (<b>2008<\/b>), 144117. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2883976\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2883976<\/a><\/li>\n<li value=\"5\">J. Hachmann, J.J. Dorando, M. Avil\u00e9s, G.K.-L. Chan, <i>The Radical Character of the Acenes: A Density Matrix Renormalization Group Study<\/i>, J. Chem. Phys. 127 (<b>2007<\/b>), 134309. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2768362\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2768362<\/a><\/li>\n<li value=\"4\">J.J. Dorando, J. Hachmann, G.K.-L. Chan, <i>Targeted Excited State Algorithms<\/i>, J. Chem. Phys. 127 (<b>2007<\/b>), 084109. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2768360\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2768360<\/a><\/li>\n<li value=\"3\">J. Hachmann, W. Cardoen, G.K.-L. Chan, <i>Multireference Correlation in Long Molecules with the Quadratic Scaling Density Matrix Renormalization Group<\/i>, J. Chem. Phys. 125 (<b>2006<\/b>), 144101. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2345196\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2345196<\/a><\/li>\n<\/ul>\n<hr class=\"style-joh1\" \/>\n<p> &nbsp; <\/p>\n<p><a id=\"catalysis\"><\/p>\n<h4>Catalysis, Reactions, and Coordination Chemistry<\/h4>\n<p><\/a> <\/p>\n<ul style=\"text-align: justify;\">\n<li value=\"30\">M.A.F. Afzal, J. Hachmann, <i>High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design<\/i>, 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 (<b>2019<\/b>), accepted. (invited) <br \/> ISBN: 978-981-120-444-9; DOI: <a href=\"https:\/\/arxiv.org\/abs\/1902.03721\" target=\"_blank\" rel=\"noopener noreferrer\"> arXiv:1902.03721<\/a> <\/li>\n<li value=\"27\">R. Asatryan, Y. Pal, J. Hachmann, E. Ruckenstein, <i>Roaming-Like Mechanism for the Dehydration of Diol Radicals<\/i>, J. Phys. Chem. A 122 (<b>2018<\/b>), 9738-9754. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1021\/acs.jpca.8b08690\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1021\/acs.jpca.8b08690<\/a> <\/li>\n<li value=\"20\">V. Kumaran Sudalayandi Rajeswari, J. Hachmann, <i>First-Principles Modeling of Polymer Degradation Kinetics and Virtual High-Throughput Screening of Candidates for Biodegradable Polymers<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>).<br \/> HDL: TBD <\/li>\n<li value=\"18\">R. Asatryan, E. Ruckenstein, J. Hachmann, <i>Revisiting the Polytopal Rearrangements in Penta-Coordinate d<sup>7<\/sup>-Metallocomplexes: Modified Berry Pseudorotation, Octahedral Switch, and Butterfly Isomerization<\/i>, Chem. Sci. 8 (<b>2017<\/b>), 5512-5525. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1039\/c7sc00703e\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1039\/c7sc00703e<\/a><\/li>\n<li value=\"10\">J. Hachmann, B.A. Frazier, P.T. Wolczanski, G.K.-L. Chan, <i>A Theoretical Study of the 3d-M(smif)<sub>2<\/sub> Complexes: Structure, Magnetism, and Oxidation States<\/i>, ChemPhysChem 12 (<b>2011<\/b>), 3236-3244. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1002\/cphc.201100286\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1002\/cphc.201100286<\/a><\/li>\n<li value=\"9\">J. Hachmann, G.K.-L. Chan, <i>Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry<\/i>, PhD Dissertation, Cornell University (<b>2010<\/b>).<br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/1813\/14774\" target=\"_blank\" rel=\"noopener noreferrer\"> 1813\/14774<\/a> <\/li>\n<\/ul>\n<hr class=\"style-joh1\" \/>\n<p> &nbsp; <\/p>\n<p><a id=\"new_methods\"><\/p>\n<h4>New Methods<\/h4>\n<p><\/a> <\/p>\n<ul style=\"text-align: justify;\">\n<li value=\"33\">M.A.F. Afzal, A. Sonpal, M. Haghighatlari, A.J. Schultz, J. Hachmann, <i>A Deep Neural Network Model for Packing Density Predictions and its Application in the Study of 1.5 Million Organic Molecules<\/i>, ChemRxiv (<b>2019<\/b>), 8217758. <br \/>DOI: <a href=\"https:\/\/doi.org\/10.26434\/chemrxiv.8217758.v1\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.26434\/chemrxiv.8217758.v1<\/a> <\/li>\n<li value=\"29\">M. Haghighatlari, J. Hachmann, <i>Advances of Machine Learning in Molecular Modeling and Simulation<\/i>, Curr. Opin. Chem. Eng. 23 (<b>2019<\/b>), 51-57. (invited) <br \/>DOI: <a href=\"https:\/\/doi.org\/10.1016\/j.coche.2019.02.009\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1016\/j.coche.2019.02.009<\/a> <\/li>\n<li value=\"25\">G. Vishwakama, J. Hachmann, <i>Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/78589\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/78589<\/a> <\/li>\n<li value=\"21\">J. Hachmann, M.A.F. Afzal, M. Haghighatlari, Y. Pal, <i>Building and Deploying a Cyberinfrastructure for the Data-Driven Design of Chemical Systems and the Exploration of Chemical Space<\/i>, Mol. Simul. 44 (<b>2018<\/b>), 921-929. (invited) <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1080\/08927022.2018.1471692\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1080\/08927022.2018.1471692<\/a><\/li>\n<li value=\"9\">J. Hachmann, G.K.-L. Chan, <i>Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry<\/i>, PhD Dissertation, Cornell University (<b>2010<\/b>).<br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/1813\/14774\" target=\"_blank\" rel=\"noopener noreferrer\"> 1813\/14774<\/a> <\/li>\n<li value=\"8\">J.J. Dorando, J. Hachmann, G.K.-L. Chan, <i>Analytic Response Theory for the Density Matrix Renormalization Group<\/i>, J. Chem. Phys. 130 (<b>2009<\/b>), 184111. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.3121422\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.3121422<\/a><\/li>\n<li value=\"7\">D. Ghosh, J. Hachmann, T. Yanai, G.K.-L. Chan, <i>Orbital Optimization in the Density Matrix Renormalization Group, with Application to Polyenes and &beta;-Carotene<\/i>, J. Chem. Phys. 128 (<b>2008<\/b>), 144117. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2883976\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2883976<\/a><\/li>\n<li value=\"6\">G.K.-L. Chan, J.J. Dorando, D. Ghosh, J. Hachmann, E. Neuscamman, H. Wang, T. Yanai, <i>An Introduction to the Density Matrix Renormalization Group Ansatz in Quantum Chemistry<\/i>, Prog. Theor. Chem. Phys. 18 (<b>2008<\/b>), 49-65. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1007\/978-1-4020-8707-3_4\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1007\/978-1-4020-8707-3_4<\/a><\/li>\n<li value=\"4\">J.J. Dorando, J. Hachmann, G.K.-L. Chan, <i>Targeted Excited State Algorithms<\/i>, J. Chem. Phys. 127 (<b>2007<\/b>), 084109. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2768360\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2768360<\/a><\/li>\n<li value=\"3\">J. Hachmann, W. Cardoen, G.K.-L. Chan, <i>Multireference Correlation in Long Molecules with the Quadratic Scaling Density Matrix Renormalization Group<\/i>, J. Chem. Phys. 125 (<b>2006<\/b>), 144101. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2345196\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2345196<\/a><\/li>\n<\/ul>\n<hr class=\"style-joh1\" \/>\n<p> &nbsp; <\/p>\n<p><a id=\"dmrg\"><\/p>\n<h4>DMRG<\/h4>\n<p><\/a> <\/p>\n<ul style=\"text-align: justify;\">\n<li value=\"9\">J. Hachmann, G.K.-L. Chan, <i>Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry<\/i>, PhD Dissertation, Cornell University (<b>2010<\/b>).<br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/1813\/14774\" target=\"_blank\" rel=\"noopener noreferrer\"> 1813\/14774<\/a> <\/li>\n<li value=\"8\">J.J. Dorando, J. Hachmann, G.K.-L. Chan, <i>Analytic Response Theory for the Density Matrix Renormalization Group<\/i>, J. Chem. Phys. 130 (<b>2009<\/b>), 184111. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.3121422\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.3121422<\/a><\/li>\n<li value=\"7\">D. Ghosh, J. Hachmann, T. Yanai, G.K.-L. Chan, <i>Orbital Optimization in the Density Matrix Renormalization Group, with Application to Polyenes and &beta;-Carotene<\/i>, J. Chem. Phys. 128 (<b>2008<\/b>), 144117. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2883976\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2883976<\/a><\/li>\n<li value=\"6\">G.K.-L. Chan, J.J. Dorando, D. Ghosh, J. Hachmann, E. Neuscamman, H. Wang, T. Yanai, <i>An Introduction to the Density Matrix Renormalization Group Ansatz in Quantum Chemistry<\/i>, Prog. Theor. Chem. Phys. 18 (<b>2008<\/b>), 49-65. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1007\/978-1-4020-8707-3_4\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1007\/978-1-4020-8707-3_4<\/a><\/li>\n<li value=\"5\">J. Hachmann, J.J. Dorando, M. Avil\u00e9s, G.K.-L. Chan, <i>The Radical Character of the Acenes: A Density Matrix Renormalization Group Study<\/i>, J. Chem. Phys. 127 (<b>2007<\/b>), 134309. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2768362\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2768362<\/a><\/li>\n<li value=\"4\">J.J. Dorando, J. Hachmann, G.K.-L. Chan, <i>Targeted Excited State Algorithms<\/i>, J. Chem. Phys. 127 (<b>2007<\/b>), 084109. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2768360\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2768360<\/a><\/li>\n<li value=\"3\">J. Hachmann, W. Cardoen, G.K.-L. Chan, <i>Multireference Correlation in Long Molecules with the Quadratic Scaling Density Matrix Renormalization Group<\/i>, J. Chem. Phys. 125 (<b>2006<\/b>), 144101. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1063\/1.2345196\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1063\/1.2345196<\/a><\/li>\n<\/ul>\n<hr class=\"style-joh1\" \/>\n<p> &nbsp; <\/p>\n<p><a id=\"other\"><\/p>\n<h4>Other Topics<\/h4>\n<p><\/a> <\/p>\n<ul style=\"text-align: justify;\">\n<li value=\"32\">K. Mukherjee, J. Hachmann, <i>Computational Modeling of Carboxylic-Based Organic Molecules for Li-Ion Battery Anode Materials<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2019<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/78667\" target=\"_blank\" rel=\"noopener noreferrer\"> TBD<\/a> <\/li>\n<li value=\"2\">J. Hachmann, N.C. Handy, <i>Nodal Hypersurfaces and Sign Domains in Many-Electron Wavefunctions<\/i>, DiplChem Thesis, University of Jena (<b>2004<\/b>).<br \/> HDL: N\/A <\/li>\n<li value=\"1\">J. Hachmann, P.T.A. Galek, T. Yanai, G.K.-L. Chan, N.C. Handy, <i>The Nodes of Hartree-Fock Wavefunctions and their Orbitals<\/i>, Chem. Phys. Lett. 392 (<b>2004<\/b>), 55-61. <br \/> DOI: <a href=\"https:\/\/doi.org\/10.1016\/j.cplett.2004.04.070\" target=\"_blank\" rel=\"noopener noreferrer\"> 10.1016\/j.cplett.2004.04.070<\/a><\/li>\n<\/ul>\n<hr class=\"style-joh1\" \/>\n<p> &nbsp; <\/p>\n<p><a id=\"theses\"><\/p>\n<h4>Theses and Dissertations<\/h4>\n<p><\/a> <\/p>\n<ul style=\"text-align: justify;\">\n<li value=\"32\">K. Mukherjee, J. Hachmann, <i>Computational Modeling of Carboxylic-Based Organic Molecules for Li-Ion Battery Anode Materials<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2019<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/78667\" target=\"_blank\" rel=\"noopener noreferrer\"> TBD<\/a> <\/li>\n<li value=\"26\">A. Sonpal, J. Hachmann, <i>Predicting Melting Points of Deep Eutectic Solvents<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/78667\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/78667<\/a> <\/li>\n<li value=\"25\">G. Vishwakama, J. Hachmann, <i>Machine Learning Model Selection for Predicting Properties of High-Refractive-Index Polymers<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/78589\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/78589<\/a> <\/li>\n<li value=\"24\">M.A.F. Afzal, J. Hachmann, <i>From Virtual High-Throughput Screening and Machine Learning to the Discovery and Rational Design of Polymers for Optical Applications<\/i>, PhD Dissertation, University at Buffalo \u2013 SUNY (<b>2018<\/b>).<br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/77967\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/77967<\/a> <\/li>\n<li value=\"20\">V. Kumaran Sudalayandi Rajeswari, J. Hachmann, <i>First-Principles Modeling of Polymer Degradation Kinetics and Virtual High-Throughput Screening of Candidates for Biodegradable Polymers<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2018<\/b>).<br \/> HDL: TBD <\/li>\n<li value=\"17\">Y. Tian, J. Hachmann, <i>Inheritance of Molecular Orbital Energies from Monomer Building Blocks to Larger Copolymers in Organic Semiconductors<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2016<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/76228\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/76228<\/a> <\/li>\n<li value=\"15\">C.-Y. Shih, J. Hachmann, <i>Systematic Trends in Results from Different Density Functional Theory Models<\/i>, MSc Thesis, University at Buffalo \u2013 SUNY (<b>2015<\/b>). <br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/10477\/51816\" target=\"_blank\" rel=\"noopener noreferrer\"> 10477\/51816<\/a> <\/li>\n<li value=\"9\">J. Hachmann, G.K.-L. Chan, <i>Ab Initio Density Matrix Renormalization Group Methodology and Computational Transition Metal Chemistry<\/i>, PhD Dissertation, Cornell University (<b>2010<\/b>).<br \/> HDL: <a href=\"http:\/\/hdl.handle.net\/1813\/14774\" target=\"_blank\" rel=\"noopener noreferrer\"> 1813\/14774<\/a> <\/li>\n<li value=\"2\">J. Hachmann, N.C. Handy, <i>Nodal Hypersurfaces and Sign Domains in Many-Electron Wavefunctions<\/i>, DiplChem Thesis, University of Jena (<b>2004<\/b>).<br \/> HDL: N\/A <\/li>\n<ol style=\"text-align: justify;\">\n<\/ul>\n<hr class=\"style-joh1\" \/>\n(Last update: 2019-06-05)<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 \u2013 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 \u2013 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 \u2013 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 \u2013 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 \u2013 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 \u2013 SUNY (2015). HDL: 10477\/51816 J. Hachmann, R. Olivares-Amaya, A. Jinich, A.L. Appleton, M.A. Blood-Forsythe, L.R. Seress, C. Rom\u00e1n-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 &ndash; 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 &ndash; 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. S\u00e1nchez-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. S\u00e1nchez-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 &nbsp; 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 \u2013 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 \u2013 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 \u2013 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 \u2013 SUNY (2015). HDL: 10477\/51816 J. Hachmann, R. Olivares-Amaya, A. Jinich, A.L. Appleton, M.A. Blood-Forsythe, L.R. Seress, C. Rom\u00e1n-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 &ndash; 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<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":27,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-428","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\/428","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=428"}],"version-history":[{"count":0,"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/pages\/428\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/pages\/27"}],"wp:attachment":[{"href":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/wp-json\/wp\/v2\/media?parent=428"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}