| 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 | 1354 | 2017 |
| Accelerating materials property predictions using machine learning G Pilania, C Wang, X Jiang, S Rajasekaran, R Ramprasad Scientific reports 3 (1), 2810, 2013 | 837 | 2013 |
| 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 | 532 | 2017 |
| Machine learning bandgaps of double perovskites G Pilania, A Mannodi-Kanakkithodi, BP Uberuaga, R Ramprasad, ... Scientific reports 6 (1), 19375, 2016 | 486 | 2016 |
| Machine learning in materials science: Recent progress and emerging applications T Mueller, AG Kusne, R Ramprasad Reviews in computational chemistry 29, 186-273, 2016 | 481 | 2016 |
| Adaptive machine learning framework to accelerate ab initio molecular dynamics V Botu, R Ramprasad International journal of quantum chemistry 115 (16), 1074-1083, 2015 | 472 | 2015 |
| Pathways towards ferroelectricity in hafnia TD Huan, V Sharma, GA Rossetti Jr, R Ramprasad Physical Review B 90 (6), 064111, 2014 | 469 | 2014 |
| Mesoporous MoO3–x Material as an Efficient Electrocatalyst for Hydrogen Evolution Reactions Z Luo, R Miao, TD Huan, IM Mosa, AS Poyraz, W Zhong, JE Cloud, ... Advanced Energy Materials 6 (16), 1600528, 2016 | 442 | 2016 |
| Polymer genome: a data-powered polymer informatics platform for property predictions C Kim, A Chandrasekaran, TD Huan, D Das, R Ramprasad The Journal of Physical Chemistry C 122 (31), 17575-17585, 2018 | 377 | 2018 |
| Machine learning strategy for accelerated design of polymer dielectrics A Mannodi-Kanakkithodi, G Pilania, TD Huan, T Lookman, R Ramprasad Scientific reports 6 (1), 1-10, 2016 | 374 | 2016 |
| Advanced polymeric dielectrics for high energy density applications TD Huan, S Boggs, G Teyssedre, C Laurent, M Cakmak, S Kumar, ... Progress in Materials Science 83, 236-269, 2016 | 363 | 2016 |
| Rational design of all organic polymer dielectrics V Sharma, C Wang, RG Lorenzini, R Ma, Q Zhu, DW Sinkovits, G Pilania, ... Nature communications 5 (1), 4845, 2014 | 326 | 2014 |
| 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 | 275 | 2019 |
| Emerging materials intelligence ecosystems propelled by machine learning R Batra, L Song, R Ramprasad Nature Reviews Materials 6 (8), 655-678, 2021 | 255 | 2021 |
| From organized high-throughput data to phenomenological theory using machine learning: the example of dielectric breakdown C Kim, G Pilania, R Ramprasad Chemistry of Materials 28 (5), 1304-1311, 2016 | 240 | 2016 |
| Learning scheme to predict atomic forces and accelerate materials simulations V Botu, R Ramprasad Physical Review B 92 (9), 094306, 2015 | 236 | 2015 |
| 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 |
| Monolithically integrated spinel MxCo3− xO4 (M= Co, Ni, Zn) nanoarray catalysts: scalable synthesis and cation manipulation for tunable low‐temperature CH4 and CO oxidation Z Ren, V Botu, S Wang, Y Meng, W Song, Y Guo, R Ramprasad, SL Suib, ... Angewandte Chemie 126 (28), 7351-7355, 2014 | 229 | 2014 |
| Magnetic properties of metallic ferromagnetic nanoparticle composites R Ramprasad, P Zurcher, M Petras, M Miller, P Renaud Journal of applied physics 96 (1), 519-529, 2004 | 226 | 2004 |
