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Real-space machine learning of correlation density functionals
Elias Polak1, Heng Zhao1, Stefan Vuckovic2
1Department of Chemistry, University of Fribourg, Fribourg, CH-1700, Switzerland.
Nature Communications
|December 1, 2025
Summary
Machine learning enhances quantum simulations by developing transferable density functional approximations (DFAs). Real-space ML models learn energy densities, improving accuracy for molecules and materials.
Area of Science:
- Quantum Chemistry
- Materials Science
- Machine Learning
Background:
- Density Functional Approximations (DFAs) are crucial for quantum simulations but lack transferability to new systems.
- Machine learning (ML) offers potential but faces challenges in improving DFA transferability.
Purpose of the Study:
- To develop highly transferable DFAs using real-space ML.
- To overcome limitations of human-designed DFAs for molecular and materials simulations.
Main Methods:
- Implemented real-space ML by learning energy densities point-by-point.
- Derived correlation energy densities from regularized perturbation theory.
- Utilized the Møller-Plesset adiabatic connection framework.
Main Results:
- Introduced Local Energy Loss for enhanced data efficiency and transferability.
- Formulated a real-space, machine-learned extension of Spin-Component-Scaled MP2 theory.
- Developed transferable DFAs that reduce self-interaction errors.
Conclusions:
- Real-space ML, combined with physically informed models, significantly improves DFA transferability.
- The developed methods provide accurate and transferable DFAs for quantum simulations.
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