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Mojtaba Haghighatlari, B.Sc. - Publications

Affiliations: 
Chemical and Biological Engineering State University of New York, Buffalo, Buffalo, NY, United States 
Area:
Computational Chemistry, Molecular Modeling, Materials Informatics, Machine Learning
Website:
http://hachmannlab.cbe.buffalo.edu/index.php/team/graduate-students/mojtaba-haghighatlari/
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8 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2023 Zhang O, Haghighatlari M, Li J, Liu ZH, Namini A, Teixeira JMC, Forman-Kay JD, Head-Gordon T. Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data. The Journal of Chemical Physics. 158. PMID 37144719 DOI: 10.1063/5.0141474  0.309
2022 Haghighatlari M, Li J, Guan X, Zhang O, Das A, Stein CJ, Heidar-Zadeh F, Liu M, Head-Gordon M, Bertels L, Hao H, Leven I, Head-Gordon T. NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. Digital Discovery. 1: 333-343. PMID 35769203 DOI: 10.1039/d2dd00008c  0.412
2020 Haghighatlari M, Li J, Heidar-Zadeh F, Liu Y, Guan X, Head-Gordon T. Learning to Make Chemical Predictions: the Interplay of Feature Representation, Data, and Machine Learning Methods. Chem. 6: 1527-1542. PMID 32695924 DOI: 10.1016/J.Chempr.2020.05.014  0.625
2020 Haghighatlari M, Vishwakarma G, Altarawy D, Subramanian R, Kota BU, Sonpal A, Setlur S, Hachmann J. 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. DOI: 10.1002/Wcms.1458  0.529
2019 Afzal MAF, Sonpal A, Haghighatlari M, Schultz AJ, Hachmann J. 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. PMID 31762970 DOI: 10.1039/C9Sc02677K  0.528
2019 Afzal MAF, Haghighatlari M, Ganesh SP, Cheng C, Hachmann J. 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. DOI: 10.1021/Acs.Jpcc.9B01147  0.739
2019 Haghighatlari M, Hachmann J. Advances of machine learning in molecular modeling and simulation Current Opinion in Chemical Engineering. 23: 51-57. DOI: 10.1016/J.Coche.2019.02.009  0.745
2018 Hachmann J, Afzal MAF, Haghighatlari M, Pal Y. Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space Molecular Simulation. 44: 921-929. DOI: 10.1080/08927022.2018.1471692  0.656
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