Publications [LaTeX]
Papers
International Journals
1 - Roadmap on Machine Learning in Electronic Structure
H. Kulik, T. Hammerschmidt, J. Schmidt, S. Botti, M.A.L. Marques, M. Boley, M. Scheffler, M. Todorović, P. Rinke, C. Oses, A. Smolyanyuk, S. Curtarolo, A. Tkatchenko, A. Bartok, S. Manzhos, M. Ihara, T. Carrington, J. Behler, O. Isayev, M. Veit, A. Grisafi, J. Nigam, M. Ceriotti, K.T. Schütt, J. Westermayr, M. Gastegger, R. Maurer, B. Kalita, K. Burke, R. Nagai, R. Akashi, O. Sugino, J. Hermann, F. Noé, S. Pilati, C. Draxl, M. Kuban, S. Rigamonti, M. Scheidgen, M. Esters, D. Hicks, C. Toher, P. Balachandran, I. Tamblyn, S. Whitelam, C. Bellinger, and L.M. Ghiringhelli
accepted for publication in Electron. Struct. (2022)
2 - A new dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals
J. Schmidt, H.-C. Wang, T.F.T. Cerqueira, S. Botti, and M.A.L. Marques
Sci. Data 9, 64 (2022)
3 - Machine-learning correction to density-functional crystal structure optimization
R. Hussein, J. Schmidt, T. Barros, M.A.L. Marques, and Silvana Botti
accepted for publication in MRS Bull. (2022)
4 - A high-throughput study of oxynitride, oxyfluoride and nitrofluoride perovskites
H.-C. Wang, J. Schmidt, S. Botti, and M.A.L. Marques
J. Mater. Chem. A 9, 8501-8513 (2021)
5 - Machine learning the derivative discontinuity of density-functional theory
J. Gedeon, J. Schmidt, M.J.P. Hodgson, J. Wetherell, C.L. Benavides-Riveros, and M.A.L. Marques
Mach. Learn.: Sci. Technol. 3, 015011 (2021)
6 - Crystal-graph attention networks for the prediction of stable materials
J. Schmidt, L. Pettersson, C. Verdozzi, S. Botti, and M.A.L. Marques
Sci. Adv. 7, eabi7948 (2021)
7 - Machine learning universal bosonic functionals
J. Schmidt, M. Fadel, and C.L. Benavides-Riveros
Phys. Rev. Research 3, L032063 (2021)
8 - Exchange-correlation functionals for band gaps of solids: Benchmark, reparametrization and machine learning
P. Borlido, J. Schmidt, A.W. Huran, F. Tran, M.A.L. Marques, and S. Botti
NPJ Comput. Mater. 6, 96 (2020)
9 - Reduced density matrix functional theory for superconductors
J. Schmidt, C.L. Benavides-Riveros, and M.A.L. Marques
Phys. Rev. B 99, 224502 (2019)
10 - Machine Learning the Physical Non-Local Exchange-Correlation Functional of
Density-Functional Theory
J. Schmidt, C.L. Benavides-Riveros, and M.A.L. Marques
J. Phys. Chem. Lett. 10, 6425-6431 (2019)
11 - Representability problem of density functional theory for superconductors
J. Schmidt, C.L. Benavides-Riveros, and M.A.L. Marques
Phys. Rev. B 99, 024502 (2019)
12 - Recent advances and applications of machine learning in solid-state materials science
J. Schmidt, M.R.G. Marques, S. Botti, and M.A.L. Marques
NPJ Comput. Mater. (also appeared in PsiK newsletter, Scientific Highlight of the Month, March) 5, 83 (2019)
13 - Predicting the stability of ternary intermetallics with density
functional theory and machine learning
J. Schmidt, L. Chen, S. Botti, and M.A.L. Marques
J. Chem. Phys. 148, 241728 (2018)
14 - Predicting the thermodynamic stability of solids combining density functional theory and machine learning
J. Schmidt, J. Shi, P. Borlido, L. Chen, S. Botti, and M.A.L. Marques
Chem. Mater. 29, 5090-5103 (2017)
Thesis
16 - Reduced Density Matrix Functional
Theory for Superconductors
J. Schmidt
Master thesis, Martin-Luther University of Halle-Wittenberg (2018)
17 - Machine Learning Prediction of the
Stability of Perovskites
J. Schmidt
Bachelor Thesis, Martin-Luther University of Halle-Wittenberg (2016)