| Machine learning in materials informatics: recent applications and prospects R Ramprasad, R Batra, G Pilania, A Mannodi-Kanakkithodi, C Kim npj Computational Materials 3 (1), 54, 2017 | 1352 | 2017 |
| Machine learning force fields: construction, validation, and outlook V Botu, R Batra, J Chapman, R Ramprasad The Journal of Physical Chemistry C 121 (1), 511-522, 2017 | 530 | 2017 |
| Physically informed artificial neural networks for atomistic modeling of materials GPP Pun, R Batra, R Ramprasad, Y Mishin Nature communications 10 (1), 2339, 2019 | 316 | 2019 |
| Solving the electronic structure problem with machine learning A Chandrasekaran, D Kamal, R Batra, C Kim, L Chen, R Ramprasad npj Computational Materials 5 (1), 22, 2019 | 274 | 2019 |
| Emerging materials intelligence ecosystems propelled by machine learning R Batra, L Song, R Ramprasad Nature Reviews Materials 6 (8), 655-678, 2021 | 254 | 2021 |
| A universal strategy for the creation of machine learning-based atomistic force fields TD Huan, R Batra, J Chapman, S Krishnan, L Chen, R Ramprasad NPJ Computational Materials 3 (1), 37, 2017 | 235 | 2017 |
| Factors favoring ferroelectricity in hafnia: A first-principles computational study R Batra, TD Huan, JL Jones, G Rossetti Jr, R Ramprasad The Journal of Physical Chemistry C 121 (8), 4139-4145, 2017 | 198 | 2017 |
| Polymer informatics: Current status and critical next steps L Chen, G Pilania, R Batra, TD Huan, C Kim, C Kuenneth, R Ramprasad Materials Science and Engineering: R: Reports 144, 100595, 2021 | 196 | 2021 |
| Dopants promoting ferroelectricity in hafnia: Insights from a comprehensive chemical space exploration R Batra, TD Huan, GA Rossetti Jr, R Ramprasad Chemistry of Materials 29 (21), 9102-9109, 2017 | 183 | 2017 |
| Machine-learning predictions of polymer properties with Polymer Genome H Doan Tran, C Kim, L Chen, A Chandrasekaran, R Batra, S Venkatram, ... Journal of Applied Physics 128 (17), 2020 | 180 | 2020 |
| Polymer design using genetic algorithm and machine learning C Kim, R Batra, L Chen, H Tran, R Ramprasad Computational Materials Science 186, 110067, 2021 | 179 | 2021 |
| Stabilization of metastable phases in hafnia owing to surface energy effects R Batra, HD Tran, R Ramprasad Applied Physics Letters 108 (17), 2016 | 152 | 2016 |
| Prediction of water stability of metal–organic frameworks using machine learning R Batra, C Chen, TG Evans, KS Walton, R Ramprasad Nature Machine Intelligence 2 (11), 704-710, 2020 | 124 | 2020 |
| Machine learning models for the lattice thermal conductivity prediction of inorganic materials L Chen, H Tran, R Batra, C Kim, R Ramprasad Computational Materials Science 170, 109155, 2019 | 124 | 2019 |
| Electrochemical stability window of polymeric electrolytes L Chen, S Venkatram, C Kim, R Batra, A Chandrasekaran, R Ramprasad Chemistry of Materials 31 (12), 4598-4604, 2019 | 123 | 2019 |
| Frequency-dependent dielectric constant prediction of polymers using machine learning L Chen, C Kim, R Batra, JP Lightstone, C Wu, Z Li, AA Deshmukh, ... npj Computational Materials 6 (1), 61, 2020 | 118 | 2020 |
| Screening of therapeutic agents for COVID-19 using machine learning and ensemble docking studies R Batra, H Chan, G Kamath, R Ramprasad, MJ Cherukara, ... The journal of physical chemistry letters 11 (17), 7058-7065, 2020 | 87 | 2020 |
| A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap A Patra, R Batra, A Chandrasekaran, C Kim, TD Huan, R Ramprasad Computational Materials Science 172, 109286, 2020 | 78 | 2020 |
| Machine learning overcomes human bias in the discovery of self-assembling peptides R Batra, TD Loeffler, H Chan, S Srinivasan, H Cui, IV Korendovych, ... Nature chemistry 14 (12), 1427-1435, 2022 | 71 | 2022 |
| Multifidelity information fusion with machine learning: A case study of dopant formation energies in hafnia R Batra, G Pilania, BP Uberuaga, R Ramprasad ACS applied materials & interfaces 11 (28), 24906-24918, 2019 | 67 | 2019 |
