On the practical applicability of modern DFT functionals for chemical computations. Case study of DM21 applicability for geometry optimization
This work addresses the efficiency of modern DFT functionals for chemical computations, specifically for geometry optimization, with incremental improvements.
The study evaluated the DM21 neural network functional for predicting molecular geometries in density functional theory, finding both potential and challenges, and proposed a solution to extend its practical applicability.
Density functional theory (DFT) is probably the most promising approach for quantum chemistry calculations considering its good balance between calculations precision and speed. In recent years, several neural network-based functionals have been developed for exchange-correlation energy approximation in DFT, DM21 developed by Google Deepmind being the most notable between them. This study focuses on evaluating the efficiency of DM21 functional in predicting molecular geometries, with a focus on the influence of oscillatory behavior in neural network exchange-correlation functionals. We implemented geometry optimization in PySCF for the DM21 functional in geometry optimization problem, compared its performance with traditional functionals, and tested it on various benchmarks. Our findings reveal both the potential and the current challenges of using neural network functionals for geometry optimization in DFT. We propose a solution extending the practical applicability of such functionals and allowing to model new substances with their help.