Muratahan Aykol
Muratahan Aykol
Google DeepMind
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Cited by
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
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
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
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
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
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
Thermodynamic limit for synthesis of metastable inorganic materials
M Aykol, SS Dwaraknath, W Sun, KA Persson
Science advances 4 (4), eaaq0148, 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
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
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
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
Machine learning for continuous innovation in battery technologies
M Aykol, P Herring, A Anapolsky
Nature Reviews Materials 5 (10), 725-727, 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
Thermodynamic Aspects of Cathode Coatings for Lithium‐Ion Batteries
M Aykol, S Kirklin, C Wolverton
Advanced Energy Materials 4 (17), 1400690, 2014
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
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
Local environment dependent method for accurate thermochemistry of transition metal compounds
M Aykol, C Wolverton
Physical Review B 90 (11), 115105, 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
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
Surface phase diagram and stability of (001) and (111) spinel oxides
S Kim, M Aykol, C Wolverton
Physical Review B 92 (11), 115411, 2015
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