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.

DM21 outperforms standard models on various benchmarks, and it's accessible as a PySCF simulation framework addition. In a paper published in Science, the model was detailed. The energy density functional component of Density Functional Theory (DFT), which describes the quantum mechanical behavior of molecules, is approximated by DM21 using a neural network. DM21 corrects systemic flaws in prior functional approximations, which failed to treat systems with "fractional electron character" appropriately. The model uses a multilayer perceptron (MLP) architecture with a grid of electron densities.

Researchers at Google subsidiary DeepMind have open-sourced DM21, a neural network model for mapping electron density to chemical interaction energy, a key component of quantum mechanical simulation. DM21 outperforms traditional models on several benchmarks and is available as an extension to the PySCF simulation framework. The model was described in an article published in Science. DM21 uses a neural network to approximate the energy density functional component of Density Functional Theory (DFT), which describes the quantum mechanical behavior of molecules. DM21 addresses systemic problems with previous functional approximations, which cannot correctly handle systems with "fractional electron character."