☰ Chemistry Tree

Johannes Hachmann, Ph.D., M.Sc., Dipl.-Chem.,

Affiliations:

Chemical and Biological Engineering

State University of New York, Buffalo, Buffalo, NY, United States

Area:

Theoretical Chemistry, Quantum Chemistry, Computational Chemistry, Molecular Modeling, Computational Materials Science

Website:

http://hachmannlab.cbe.buffalo.edu/index.php/team/johannes-hachmann/

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"Johannes Hachmann"

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Parents

Ulrich-Walter Grummt	research assistant	2001-2002	Universität Jena
James F. Weston	research assistant	2003-2003	Universität Jena
Hans-Gerhard Fritsche	research assistant	2003-2004	Universität Jena
Nicholas C. Handy	research assistant	2003-2004	Cambridge
Garnet K.L. Chan	grad student	2004-2010	Cornell
(Ab initio density matrix renormalization group methodology and computational transition metal chemistry.)
Alán Aspuru-Guzik	post-doc	2009-2014	Harvard

Children

Sai Prasad Ganesh	research assistant	2014-	SUNY Buffalo
Zachary Manzer	research assistant	2014-	SUNY Buffalo
Bryan A. Moore	research assistant	2014-	SUNY Buffalo
Sykhere Brown	research assistant	2017-	SUNY Buffalo
Mohammad Atif Faiz Afzal	grad student	2014-	SUNY Buffalo
Mojtaba Haghighatlari	grad student	2014-	SUNY Buffalo
Jun Pan	grad student	2014-	SUNY Buffalo
Ching-Yen Shih	grad student	2014-	SUNY Buffalo
Shawn S. Zadeh	grad student	2014-	SUNY Buffalo
Aditya Sonpal	grad student	2016-	SUNY Buffalo
Andrew J. Schultz	research scientist	2014-	SUNY Buffalo

Collaborators

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Publications

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Haghighatlari M, Vishwakarma G, Altarawy D, et al. (2020) ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data Wiley Interdisciplinary Reviews: Computational Molecular Science. 10
Afzal MAF, Sonpal A, Haghighatlari M, et al. (2019) A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules. Chemical Science. 10: 8374-8383
Afzal MAF, Hachmann J. (2019) Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers. Physical Chemistry Chemical Physics : Pccp
Afzal MAF, Haghighatlari M, Ganesh SP, et al. (2019) Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining The Journal of Physical Chemistry C. 123: 14610-14618
Haghighatlari M, Hachmann J. (2019) Advances of machine learning in molecular modeling and simulation Current Opinion in Chemical Engineering. 23: 51-57
Afzal MAF, Cheng C, Hachmann J. (2018) Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers. The Journal of Chemical Physics. 148: 241712
Hachmann J, Afzal MAF, Haghighatlari M, et al. (2018) Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space Molecular Simulation. 44: 921-929
Lopez SA, Pyzer-Knapp EO, Simm GN, et al. (2016) The Harvard organic photovoltaic dataset. Scientific Data. 3: 160086
Hachmann J, Olivares-Amaya R, Jinich A, et al. (2014) Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry-the Harvard Clean Energy Project Energy and Environmental Science. 7: 698-704
Amador-Bedolla C, Olivares-Amaya R, Hachmann J, et al. (2013) Organic Photovoltaics Informatics For Materials Science and Engineering: Data-Driven Discovery For Accelerated Experimentation and Application. 423-442

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