Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation

Maksim Kulichenko; Kipton Barros; Nicholas Lubbers; Nikita Fedik; Guoqing Zhou; Sergei Tretiak; Benjamin Nebgen; Anders M. N. Niklasson

arXiv · 2303.00689

Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation

Maksim Kulichenko·Kipton Barros·Nicholas Lubbers·Nikita Fedik·Guoqing Zhou·Sergei Tretiak·Benjamin Nebgen·Anders M. N. Niklasson

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Abstract

Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures, canonical density matrix perturbation theory, and an adaptive Krylov Subspace Approximation for the integration of the electronic equations of motion within the XL-BOMB approach (KSA-XL-BOMD). The PyTorch implementation leverages the use of GPU and machine learning hardware accelerators for the simulations. The new XL-BOMD formulation allows studying more challenging chemical systems with charge instabilities and low electronic energy gaps. Current public release of PySeQM continues our development of modular architecture for large-scale simulations employing semiempirical quantum mechanical treatment. Applied to molecular dynamics simulation of 840 carbon atoms, one integration time step executes in 4 seconds on a single Nvidia RTX A6000 GPU.

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Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Nikita Fedik, Guoqing Zhou, Sergei Tretiak, Benjamin Nebgen, Anders M. N. Niklasson. 2023-03-01. Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation. https://arxiv.org/abs/2303.00689

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