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PYSEQM

an interface to semi-empirical quantum chemistry methods implemented with pytorch

README

[PYSEQM: PYtorch-based Semi-Empirical Quantum Mechanics]

License: BSD-3
Website & User Manual

PYSEQM is a Semi-Empirical Quantum Mechanics package implemented in PyTorch. It provides built-in interfaces for machine learning and efficient molecular dynamic engines with GPU supported. Several molecular dynamics algorithms are implemented for facilitating dynamic simulations, inlcuding orginal and Extended Lagrangian Born-Oppenheimer Molecular Dynamics, geometric optimization and several thermostats.

Please visit the PYSEQM website for a user guide of PYSEQM.

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Features:

  • Interface with machine learning (ML) framework like HIPNN for ML applications and development.
  • GPU-supported Molecular Dynamics Engine
  • Stable and Efficient Extended Lagrangian Born Oppenheimer Molecular Dynamics (XL-BOMD)
    • Includes Krylov Subspace Approximation (KSA-XL-BOMD) for more accurate density matrix propagation and handling small HOMO-LUMO gaps
  • Excited states with Configuration Interaction Singles (CIS) and Time-Dependent Hartree-Fock (TDHF)
  • Efficient expansion algorithm SP2 for faster calculation of the density matrix

Installation:

git clone https://github.com/lanl/PYSEQM.git
cd PYSEQM
pip install .

or

pip install git+https://github.com/lanl/PYSEQM.git

To enable GPU with CUDA, please refer to the Installation Guide on PyTorch website

Prerequisites:

  • PyTorch>=1.9

Usage:

see PYSEQM website

trained model file

model.pt

Semi-Empirical Methods Implemented:

  1. MNDO
  2. AM1
  3. PM3
  4. PM6

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Authors:

Maksim Kulichenko, Guoqing Zhou, Benjamin Nebgen, Vishikh Athavale, Nikita Fedik, Nicholas Lubbers, Walter Malone, Anders M. N. Niklasson and Sergei Tretiak

Citation:

  1. Zhou, Guoqing, et al. "Graphics processing unit-accelerated semiempirical Born Oppenheimer molecular dynamics using PyTorch." Journal of Chemical Theory and Computation 16.8 (2020): 4951-4962.
  2. Kulichenko, Maksim, et al. "Semi-Empirical Shadow Molecular Dynamics: A PyTorch implementation." Journal of Chemical Theory and Computation 19.11 (2023): 3209–3222.
  3. Zhou, Guoqing, et al. "Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics." Proceedings of the National Academy of Sciences 119.27 (2022): e2120333119.

Acknowledgments:

Los Alamos National Lab (LANL), Center for Nonlinear Studies (CNLS), T-1

© (or copyright) 2020. Triad National Security, LLC. All rights reserved.
This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos
National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.
Department of Energy/National Nuclear Security Administration. All rights in the program are
reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear
Security Administration. The Government is granted for itself and others acting on its behalf a
nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare
derivative works, distribute copies to the public, perform publicly and display publicly, and to permit
others to do so.

License:

This program is open source under the BSD-3 License.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this
    list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice,
    this list of conditions and the following disclaimer in the documentation
    and/or other materials provided with the distribution.

  3. Neither the name of the copyright holder nor the names of its
    contributors may be used to endorse or promote products derived from
    this software without specific prior written permission.

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