Machine-learned density functional based quantum chemical computations for ethane: performance of DeepMind 21 on potential energy surface and molecular properties.

contextMachine learning (ML) has proven to be a promising method in quantum chemistry calculations. Researchers at Google DeepMind established the superior performance of a machine-learned functional DeepMind 21 (DM21), the neural network-based functionals which emerged as a powerful tool for developing exchange-correlation energy approximation in density functional theory (DFT), to accurately explain the charge delocalization and strong correlation. While many researchers have cited the work of DeepMind, still there remains a paucity of research publications that perform algorithmic computations with DeepMind's AI model for quantum sciences. To address this lacuna, this paper investigates quantum chemistry algorithmic computations of potential energy surface and analysis properties of ethane molecule (C

methodsIn this method, the potential energy surfaces and properties (dipole moment, molecular orbitals) are computed using machine-learned density function approximation using pretrained deep learning models provided by DeepMind 21 researchers. By inserting deep learning inference in density functional theory (DFT) with a pretrained neural network, self-consistent field (SCF) energy at different geometries along the coordinate of interest is computed, and hence potential energy surfaces are computed and obtained. For a given specified molecular geometry, the algorithm computes the electron density vector, which is then used as a machine learning feature input for a pretrained DM21 deep learning model to predict the exchange-correlation. This methodology was implemented in a Python source code using frameworks such as PySCF and DM21.