| Advances of machine learning in molecular modeling and simulation M Haghighatlari, J Hachmann Current Opinion in Chemical Engineering 23, 51-57, 2019 | 134 | 2019 |
| Newtonnet: A newtonian message passing network for deep learning of interatomic potentials and forces M Haghighatlari, J Li, X Guan, O Zhang, A Das, CJ Stein, F Heidar-Zadeh, ... Digital Discovery 1 (3), 333-343, 2022 | 109 | 2022 |
| Learning to Make Chemical Predictions: The Interplay of Feature Representation, Data, and Machine Learning Methods M Haghighatlari, J Li, F Heidar-Zadeh, Y Liu, X Guan, T Head-Gordon Chem 6 (7), 1527-1542, 2020 | 99 | 2020 |
| ChemML: A Machine Learning and Informatics Program Package for the Analysis, Mining, and Modeling of Chemical and Materials Data M Haghighatlari, G Vishwakarma, D Altarawy, R Subramanian, BU Kota, ... WIREs Computational Molecular Science 10 (4), e1458, 2020 | 89 | 2020 |
| Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining MAF Afzal, M Haghighatlari, S Prasad Ganesh, C Cheng, J Hachmann The Journal of Physical Chemistry C 123 (23), 14610-14618, 2019 | 60 | 2019 |
| Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states J Lincoff, M Haghighatlari, M Krzeminski, JMC Teixeira, GNW Gomes, ... Communications chemistry 3 (1), 74, 2020 | 54 | 2020 |
| Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space J Hachmann, MAF Afzal, M Haghighatlari, Y Pal Molecular Simulation 44 (11), 921-929, 2018 | 50 | 2018 |
| A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules MAF Afzal, A Sonpal, M Haghighatlari, AJ Schultz, J Hachmann Chemical Science 10 (36), 8374-8383, 2019 | 46 | 2019 |
| IDPConformerGenerator: A Flexible Software Suite for Sampling Conformational Space of Disordered Protein States JMC Teixeira, ZH Liu, A Namini, J Li, RM Vernon, M Krzeminski, ... The Journal of Physical Chemistry A 126 (35), 5985-6003, 2022 | 36 | 2022 |
| Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data O Zhang, M Haghighatlari, J Li, ZH Liu, A Namini, J Teixeira, ... The Journal of Chemical Physics 158 (17), 2023 | 26 | 2023 |
| Thinking globally, acting locally: on the issue of training set imbalance and the case for local machine learning models in chemistry M Haghighatlari, CY Shih, J Hachmann https://doi.org/10.26434/chemrxiv.8796947.v2, 2019 | 21 | 2019 |
| A benchmark dataset for Hydrogen Combustion X Guan, A Das, CJ Stein, F Heidar-Zadeh, L Bertels, M Liu, ... Scientific data 9 (1), 215, 2022 | 19 | 2022 |
| Towards autonomous machine learning in chemistry via evolutionary algorithms G Vishwakarma, M Haghighatlari, J Hachmann https://doi.org/10.26434/chemrxiv.9782387.v1, 2019 | 17 | 2019 |
| A physics-infused deep learning model for the prediction of refractive indices and its use for the large-scale screening of organic compound space M Haghighatlari, G Vishwakarma, MAF Afzal, J Hachmann https://doi.org/10.26434/chemrxiv.8796950.v1, 2019 | 17 | 2019 |
| Open chemistry, JupyterLab, REST, and quantum chemistry MD Hanwell, C Harris, A Genova, M Haghighatlari, M El Khatib, P Avery, ... International Journal of Quantum Chemistry 121 (1), e26472, 2021 | 16 | 2021 |
| Low-temperature gas–solid carbonation of magnesia and magnesium hydroxide promoted by non-immersive contact with water J Highfield, J Chen, M Haghighatlari, J Åbacka, R Zevenhoven RSC Advances 6 (92), 89655-89664, 2016 | 16 | 2016 |
| Protein dynamics to define and refine disordered protein ensembles PM Naullage, M Haghighatlari, A Namini, JMC Teixeira, J Li, O Zhang, ... The Journal of Physical Chemistry B 126 (9), 1885-1894, 2022 | 9 | 2022 |
| C.; Gomes, G.-NW; Gradinaru, CC; Forman-Kay, JD; Head-Gordon, T. Extended experimental inferential structure determination method in determining the structural ensembles of … J Lincoff, M Haghighatlari, M Krzeminski, JM Teixeira Commun. Chem 3 (1), 74, 2020 | 9 | 2020 |
| Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies M Haghighatlari State University of New York at Buffalo, 2019 | 9 | 2019 |
| Framing the Role of Big Data and Modern Data Science in Chemistry J Hachmann, T Windus, J McLean, V Allwardt, SR Alexandra, MAF Afzal, ... https://www.nsf.gov/mps/che/workshops …, 2018 | 9 | 2018 |
Dr. Johannes HachmannAssociate Professor in the Department of Chemical and Biological Engineering, University at BuffaloVerifisert e-postadresse på buffalo.edu