Exchange-correlation functional development: Data-driven and physically-constrained
POSTER
Abstract
We present a methodology that combines data science and physical constraints for the development of new exchange-correlation functionals [1].
We aim for multi-purpose functionals that are applicable to compute a wide range of physical properties with optimal accuracy and transferability.
In this talk, we present the general methodology which lead to the MCML functional [1], its modifications as well as their performance on a number of data sets.
[1] K. Brown et al., J. Comput. Chem., vol. 42, 2004, 2021
We aim for multi-purpose functionals that are applicable to compute a wide range of physical properties with optimal accuracy and transferability.
In this talk, we present the general methodology which lead to the MCML functional [1], its modifications as well as their performance on a number of data sets.
[1] K. Brown et al., J. Comput. Chem., vol. 42, 2004, 2021
*This research was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division, Catalysis Science Program to the SUNCAT Center for Interface Science and Catalysis.
Publication: K. Brown et al., J. Comput. Chem., vol. 42, 2004, 2021
Presenters
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Kai Trepte
- Stanford University