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
2264 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
1968 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
1742 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
797 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
719 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
475 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
352 2017 Thermodynamic limit for synthesis of metastable inorganic materials M Aykol, SS Dwaraknath, W Sun, KA Persson
Science advances 4 (4), eaaq0148, 2018
336 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
226 2018 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
193 2016 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
190 2021 Machine learning for continuous innovation in battery technologies M Aykol, P Herring, A Anapolsky
Nature Reviews Materials 5 (10), 725-727, 2020
183 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
155 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
144 2020 Thermodynamic Aspects of Cathode Coatings for Lithium‐Ion Batteries M Aykol, S Kirklin, C Wolverton
Advanced Energy Materials 4 (17), 1400690, 2014
136 2014 Local environment dependent M Aykol, C Wolverton
Physical Review B 90 (11), 115105, 2014
134 2014 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
133 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
131 2015 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
130 2020 Surface phase diagram and stability of (001) and (111) S Kim, M Aykol, C Wolverton
Physical Review B 92 (11), 115411, 2015
112 2015 
Chris WolvertonProfessor of Materials Science and Eng., Northwestern UniversityEmail verificata su northwestern.edu