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. Haghighatlari, J. Hachmann, Advances of machine learning in molecular modeling and simulation, Curr. Opin. Chem. Eng. (2019), submitted. DOI: TBD.
  • M.A.F. Afzal, J. Hachmann, High-throughput computational studies in catalysis and materials research, and their impact on rational design, in Big Data Methods in Experimental Materials Discovery, S. Kalidindi, T. Lookman (Eds.), World Scientific, Singapore (2019), submitted. ISBN: TBD.
  • A. Sonpal, J. Hachmann, Predicting melting points of deep eutectic solvents, MSc thesis, University at Buffalo – SUNY (2018).
  • M.A.F. Afzal, J. Hachmann, Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers, ChemRxiv (2018), 6934343.v1. DOI: 10.26434/chemrxiv.6934343.v1
  • G. Vishwakama, J. Hachmann, Machine learning model selection for predicting properties of organic polymers, MSc thesis, University at Buffalo – SUNY (2018).
  • 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).
  • 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).
  • 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. 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).
  • 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.
  • 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).
  • 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.
  • C.-Y. Shih, J. Hachmann, Systematic trends in results from different density functional theory models, MSc thesis, University at Buffalo – SUNY (2015).
  • 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.
  • 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.
  • 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.
  • 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.

 

Organic Electronics and Optical Polymers

  • M.A.F. Afzal, J. Hachmann, Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers, ChemRxiv (2018), 6934343.v1. DOI: 10.26434/chemrxiv.6934343.v1
  • G. Vishwakama, J. Hachmann, Machine learning model selection for predicting properties of organic polymers, MSc thesis, University at Buffalo – SUNY (2018).
  • 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).
  • 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).
  • 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.
  • C.-Y. Shih, J. Hachmann, Systematic trends in results from different density functional theory models, MSc thesis, University at Buffalo – SUNY (2015).
  • 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.
  • 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.
  • 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.
  • 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.
  • J. Hachmann, G.K.-L. Chan, Ab initio density matrix renormalization group methodology and computational transition metal chemistry, PhD dissertation, Cornell University (2010).
  • 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.
  • 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.

 

Catalysis, Reactions, and Coordination Chemistry

  • 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).
  • 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.
  • 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.
  • J. Hachmann, G.K.-L. Chan, Ab initio density matrix renormalization group methodology and computational transition metal chemistry, PhD dissertation, Cornell University (2010).

 

New Methods

  • J. Hachmann, G.K.-L. Chan, Ab initio density matrix renormalization group methodology and computational transition metal chemistry, PhD dissertation, Cornell University (2010).
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Analytic response theory for the density matrix renormalization group, J. Chem. Phys. 130 (2009), 184111.
  • 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.
  • 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.
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Targeted excited state algorithms, J. Chem. Phys. 127 (2007), 084109.
  • 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.

 

DMRG

  • J. Hachmann, G.K.-L. Chan, Ab initio density matrix renormalization group methodology and computational transition metal chemistry, PhD dissertation, Cornell University (2010).
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Analytic response theory for the density matrix renormalization group, J. Chem. Phys. 130 (2009), 184111.
  • 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.
  • 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.
  • 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.
  • J.J. Dorando, J. Hachmann, G.K.-L. Chan, Targeted excited state algorithms, J. Chem. Phys. 127 (2007), 084109.
  • 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.

 

Other Topics

  • J. Hachmann, N.C. Handy, Nodal hypersurfaces and sign domains in many-electron wavefunctions, DiplChem thesis, University of Jena (2004).
  • 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.

 

Theses and Dissertations

  • A. Sonpal, J. Hachmann, Predicting melting points of deep eutectic solvents, MSc thesis, University at Buffalo – SUNY (2018).
  • G. Vishwakama, J. Hachmann, Machine learning model selection for predicting properties of organic polymers, MSc thesis, University at Buffalo – SUNY (2018).
  • 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).
  • 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).
  • 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).
  • C.-Y. Shih, J. Hachmann, Systematic trends in results from different density functional theory models, MSc thesis, University at Buffalo – SUNY (2015).
  • J. Hachmann, G.K.-L. Chan, Ab initio density matrix renormalization group methodology and computational transition metal chemistry, PhD dissertation, Cornell University (2010).
  • J. Hachmann, N.C. Handy, Nodal hypersurfaces and sign domains in many-electron wavefunctions, DiplChem thesis, University of Jena (2004).