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Jonathan Schmidt
Jonathan Schmidt
ETH Zurich
Geverifieerd e-mailadres voor mat.ethz.ch
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Recent advances and applications of machine learning in solid-state materials science
J Schmidt, MRG Marques, S Botti, MAL Marques
npj Computational Materials 5 (1), 1-36, 2019
20252019
Predicting the thermodynamic stability of solids combining density functional theory and machine learning
J Schmidt, J Shi, P Borlido, L Chen, S Botti, MAL Marques
Chemistry of Materials 29 (12), 5090-5103, 2017
3482017
Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning
P Borlido, J Schmidt, AW Huran, F Tran, MAL Marques, S Botti
npj Computational Materials 6 (1), 1-17, 2020
2742020
Roadmap on Machine Learning in Electronic Structure
H Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ...
Electronic Structure, 2022
1522022
Crystal graph attention networks for the prediction of stable materials
J Schmidt, L Pettersson, C Verdozzi, S Botti, MAL Marques
Science Advances 7 (49), eabi7948, 2021
1102021
Machine Learning the Physical Nonlocal Exchange–Correlation Functional of Density-Functional Theory
J Schmidt, CL Benavides-Riveros, MAL Marques
Journal of Physical Chemistry Letters, 2019
952019
Machine-learning-assisted determination of the global zero-temperature phase diagram of materials.
J Schmidt, N Hoffmann, HC Wang, P Borlido, PJMA Carriço, ...
Advanced Materials, e2210788-e2210788, 2023
58*2023
Predicting the stability of ternary intermetallics with density functional theory and machine learning
J Schmidt, L Chen, S Botti, MAL Marques
The Journal of chemical physics 148 (24), 2018
502018
Machine learning the derivative discontinuity of density-functional theory
J Gedeon, J Schmidt, MJP Hodgson, J Wetherell, CL Benavides-Riveros, ...
Machine Learning: Science and Technology 3 (1), 015011, 2021
332021
Reduced density matrix functional theory for superconductors
J Schmidt, CL Benavides-Riveros, MAL Marques
Physical Review B 99 (22), 224502, 2019
312019
A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals
J Schmidt, HC Wang, TFT Cerqueira, MAL Marques
Scientific Data 9 (64), 2022
282022
High-throughput study of oxynitride, oxyfluoride and nitrofluoride perovskites
H Wang, J Schmidt, S Botti, MAL Marques
Journal of Materials Chemistry A, 2021
282021
Superconductivity in antiperovskites
N Hoffmann, T Cerqueira, J Schmidt, M Marques
npj Computational Materials 8 (1), 2022
242022
Improving machine-learning models in materials science through large datasets
J Schmidt, TFT Cerqueira, AH Romero, A Loew, F Jäger, HC Wang, ...
Materials Today Physics, 101560, 2024
172024
Machine learning universal bosonic functionals
J Schmidt, M Fadel, CL Benavides-Riveros
Physical Review Research 3 (3), L032063, 2021
172021
Transfer learning on large datasets for the accurate prediction of material properties
N Hoffmann, J Schmidt, S Botti, MAL Marques
Digital Discovery, 2023
142023
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
M Evans, J Bergsma, A Merkys, C Andersen, OB Andersson, D Beltrán, ...
Digital Discovery, 2024
122024
Machine-learning correction to density-functional crystal structure optimization
R Hussein, J Schmidt, T Barros, MAL Marques, S Botti
MRS Bulletin 47 (8), 765-771, 2022
122022
Searching for ductile superconducting Heusler X2YZ compounds
N Hoffmann, TFT Cerqueira, P Borlido, A Sanna, J Schmidt, MAL Marques
npJ computational materials 9 (138), 2023
82023
Symmetry-based computational search for novel binary and ternary 2D materials
HC Wang, J Schmidt, MAL Marques, L Wirtz, AH Romero
2D Materials 10 (3), 035007, 2023
72023
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Artikelen 1–20