Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System
Conference
·
· Proceedings of Machine Learning Research
OSTI ID:2481667
- Department of Applied Mathematics, University of California Merced
- Department of Physics, University of California Merced
- Department of Applied Mathematics, Department of Chemistry and Biochemistry, University of California Merced
- Department of Chemistry and Biochemistry, University of California Merced
We develop methods to learn the correlation potential for a time-dependent Kohn-Sham (TDKS) system in one spatial dimension. We start from a low-dimensional two-electron system for which we can numerically solve the time-dependent Schr¨odinger equation; this yields electron densities suitable for training models of the correlation potential. We frame the learning problem as one of optimizing a least-squares objective subject to the constraint that the dynamics obey the TDKS equation. Applying adjoints, we develop efficient methods to compute gradients and thereby learn models of the correlation potential. Our results show that it is possible to learn values of the correlation potential such that the resulting electron densities match ground truth densities. We also show how to learn correlation potential functionals with memory, demonstrating one such model that yields reasonable results for trajectories outside the training set.
- Research Organization:
- University of California Merced
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- DOE Contract Number:
- SC0020203;
- OSTI ID:
- 2481667
- Resource Type:
- Conference paper/presentation
- Conference Information:
- Journal Name: Proceedings of Machine Learning Research Journal Volume: 168
- Country of Publication:
- United States
- Language:
- English
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