| Reproducibility in density functional theory calculations of solids K Lejaeghere, G Bihlmayer, T Björkman, P Blaha, S Blügel, V Blum, ... Science 351 (6280), aad3000, 2016 | 1112 | 2016 |
| Mj vansetten, v. van speybroeck, jm wills, jr yates, gx zhang, s. cottenier, reproducibility in density functional theory calculations of solids K Lejaeghere, G Bihlmayer, T Bjorkman, P Blaha, S Blugel, V Blum, ... Science 351, 1415ŰU1481, 2016 | 492* | 2016 |
| Recent advances and applications of deep learning methods in materials science K Choudhary, B DeCost, C Chen, A Jain, F Tavazza, R Cohn, CW Park, ... npj Computational Materials 8 (1), 59, 2022 | 281 | 2022 |
| Considerations for choosing and using force fields and interatomic potentials in materials science and engineering CA Becker, F Tavazza, ZT Trautt, RAB de Macedo Current Opinion in Solid State and Materials Science 17 (6), 277-283, 2013 | 276 | 2013 |
| The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design K Choudhary, KF Garrity, ACE Reid, B DeCost, AJ Biacchi, ... npj computational materials 6 (1), 173, 2020 | 246 | 2020 |
| Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning D Jha, K Choudhary, F Tavazza, W Liao, A Choudhary, C Campbell, ... Nature communications 10 (1), 5316, 2019 | 235 | 2019 |
| High-throughput identification and characterization of two-dimensional materials using density functional theory K Choudhary, I Kalish, R Beams, F Tavazza Scientific reports 7 (1), 5179, 2017 | 233 | 2017 |
| Characterization of Few-Layer 1T′ MoTe2 by Polarization-Resolved Second Harmonic Generation and Raman Scattering R Beams, LG Cançado, S Krylyuk, I Kalish, B Kalanyan, AK Singh, ... ACS nano 10 (10), 9626-9636, 2016 | 167 | 2016 |
| Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape K Choudhary, B DeCost, F Tavazza Physical review materials 2 (8), 083801, 2018 | 139 | 2018 |
| Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics RK Vasudevan, K Choudhary, A Mehta, R Smith, G Kusne, F Tavazza, ... MRS communications 9 (3), 821-838, 2019 | 133 | 2019 |
| MPInterfaces: A Materials Project based Python tool for high-throughput computational screening of interfacial systems K Mathew, AK Singh, JJ Gabriel, K Choudhary, SB Sinnott, AV Davydov, ... Computational Materials Science 122, 183-190, 2016 | 133 | 2016 |
| Elastic properties of bulk and low-dimensional materials using van der Waals density functional K Choudhary, G Cheon, E Reed, F Tavazza Physical Review B 98 (1), 014107, 2018 | 126 | 2018 |
| Accelerated discovery of efficient solar cell materials using quantum and machine-learning methods K Choudhary, M Bercx, J Jiang, R Pachter, D Lamoen, F Tavazza Chemistry of materials 31 (15), 5900-5908, 2019 | 108 | 2019 |
| Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations K Choudhary, F Tavazza Computational materials science 161, 300-308, 2019 | 100 | 2019 |
| Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms K Choudhary, Q Zhang, ACE Reid, S Chowdhury, N Van Nguyen, Z Trautt, ... Scientific data 5 (1), 1-12, 2018 | 99 | 2018 |
| New epitaxially stabilized CoSi phase with the CsCl structure H Von Kaenel, C Schwarz, S Goncalves-Conto, E Müller, L Miglio, ... Physical review letters 74 (7), 1163, 1995 | 97 | 1995 |
| The structural phases and vibrational properties of Mo1− xWxTe2 alloys SM Oliver, R Beams, S Krylyuk, I Kalish, AK Singh, A Bruma, F Tavazza, ... 2D Materials 4 (4), 045008, 2017 | 83 | 2017 |
| Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data V Gupta, K Choudhary, F Tavazza, C Campbell, W Liao, A Choudhary, ... Nature communications 12 (1), 6595, 2021 | 82 | 2021 |
| Electronically Induced Atom Motion in Engineered CoCun Nanostructures JA Stroscio, F Tavazza, JN Crain, RJ Celotta, AM Chaka Science 313 (5789), 948-951, 2006 | 79 | 2006 |
| High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses K Choudhary, KF Garrity, V Sharma, AJ Biacchi, AR Hight Walker, ... npj computational materials 6 (1), 64, 2020 | 73 | 2020 |
