Mojtaba Haghighatlari, B.Sc.

Chemical and Biological Engineering State University of New York, Buffalo, Buffalo, NY, United States 
Computational Chemistry, Molecular Modeling, Materials Informatics, Machine Learning
"Mojtaba Haghighatlari"
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Haghighatlari M, Li J, Guan X, et al. (2022) NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. Digital Discovery. 1: 333-343
Haghighatlari M, Li J, Heidar-Zadeh F, et al. (2020) Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Methods. Chem. 6: 1527-1542
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, 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
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
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