| Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD) JE Saal, S Kirklin, M Aykol, B Meredig, C Wolverton Jom 65, 1501-1509, 2013 | 2368 | 2013 |
| Data-driven prediction of battery cycle life before capacity degradation KA Severson, PM Attia, N Jin, N Perkins, B Jiang, Z Yang, MH Chen, ... Nature Energy 4 (5), 383-391, 2019 | 2116 | 2019 |
| The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies S Kirklin, JE Saal, B Meredig, A Thompson, JW Doak, M Aykol, S Rühl, ... npj Computational Materials 1 (1), 1-15, 2015 | 1833 | 2015 |
| Closed-loop optimization of fast-charging protocols for batteries with machine learning PM Attia, A Grover, N Jin, KA Severson, TM Markov, YH Liao, MH Chen, ... Nature 578 (7795), 397-402, 2020 | 859 | 2020 |
| Accelerating the discovery of materials for clean energy in the era of smart automation DP Tabor, LM Roch, SK Saikin, C Kreisbeck, D Sheberla, JH Montoya, ... Nature reviews materials 3 (5), 5-20, 2018 | 745 | 2018 |
| Scaling deep learning for materials discovery A Merchant, S Batzner, SS Schoenholz, M Aykol, G Cheon, ED Cubuk Nature 624 (7990), 80-85, 2023 | 624 | 2023 |
| Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows K Mathew, JH Montoya, A Faghaninia, S Dwarakanath, M Aykol, H Tang, ... Computational Materials Science 139, 140-152, 2017 | 370 | 2017 |
| Thermodynamic limit for synthesis of metastable inorganic materials M Aykol, SS Dwaraknath, W Sun, KA Persson Science advances 4 (4), eaaq0148, 2018 | 358 | 2018 |
| A machine learning approach for engineering bulk metallic glass alloys L Ward, SC O'Keeffe, J Stevick, GR Jelbert, M Aykol, C Wolverton Acta Materialia 159, 102-111, 2018 | 234 | 2018 |
| Perspective—combining physics and machine learning to predict battery lifetime M Aykol, CB Gopal, A Anapolsky, PK Herring, B van Vlijmen, MD Berliner, ... Journal of The Electrochemical Society 168 (3), 030525, 2021 | 210 | 2021 |
| High-throughput computational design of cathode coatings for Li-ion batteries M Aykol, S Kim, VI Hegde, D Snydacker, Z Lu, S Hao, S Kirklin, D Morgan, ... Nature communications 7 (1), 13779, 2016 | 200 | 2016 |
| Machine learning for continuous innovation in battery technologies M Aykol, P Herring, A Anapolsky Nature Reviews Materials 5 (10), 725-727, 2020 | 193 | 2020 |
| Active learning for accelerated design of layered materials L Bassman Oftelie, P Rajak, RK Kalia, A Nakano, F Sha, J Sun, DJ Singh, ... npj Computational Materials 4 (1), 74, 2018 | 157 | 2018 |
| High-throughput study of lattice thermal conductivity in binary rocksalt and zinc blende compounds including higher-order anharmonicity Y Xia, VI Hegde, K Pal, X Hua, D Gaines, S Patel, J He, M Aykol, ... Physical Review X 10 (4), 041029, 2020 | 152 | 2020 |
| Thermodynamic Aspects of Cathode Coatings for Lithium‐Ion Batteries M Aykol, S Kirklin, C Wolverton Advanced Energy Materials 4 (17), 1400690, 2014 | 137 | 2014 |
| Benchmarking the acceleration of materials discovery by sequential learning B Rohr, HS Stein, D Guevarra, Y Wang, JA Haber, M Aykol, SK Suram, ... Chemical science 11 (10), 2696-2706, 2020 | 136 | 2020 |
| Local environment dependent method for accurate thermochemistry of transition metal compounds M Aykol, C Wolverton Physical Review B 90 (11), 115105, 2014 | 136 | 2014 |
| Van der Waals interactions in layered lithium cobalt oxides M Aykol, S Kim, C Wolverton The Journal of Physical Chemistry C 119 (33), 19053-19058, 2015 | 134 | 2015 |
| Controlling the intercalation chemistry to design high-performance dual-salt hybrid rechargeable batteries JH Cho, M Aykol, S Kim, JH Ha, C Wolverton, KY Chung, KB Kim, BW Cho Journal of the American Chemical Society 136 (46), 16116-16119, 2014 | 134 | 2014 |
| Active learning accelerated discovery of stable iridium oxide polymorphs for the oxygen evolution reaction RA Flores, C Paolucci, KT Winther, A Jain, JAG Torres, M Aykol, ... Chemistry of Materials 32 (13), 5854-5863, 2020 | 114 | 2020 |
Chris WolvertonProfessor of Materials Science and Eng., Northwestern Universityایمیل تأیید شده در northwestern.edu