About PYSEQM#
PYSEQM is a powerful and user-friendly simulation tool built on PyTorch, designed to help researchers model molecular behavior with speed and efficiency. Leveraging GPU acceleration, PYSEQM allows physicists and chemists to simulate atomic motion, optimize molecular geometries, and explore how molecular systems respond to varying environmental conditions. Its seamless integration with PyTorch makes it especially suitable for combining traditional molecular simulations with modern machine learning techniques, opening up new possibilities in computational chemistry and materials science.
How to Cite#
If you use PYSEQM in your research, please cite the following publications:
Zhou, G., Nebgen, B., Lubbers, N., Malone, W., Niklasson, A. M. N., & Tretiak, S. (2020). Graphics processing unit-accelerated semiempirical Born–Oppenheimer molecular dynamics using PyTorch. Journal of Chemical Theory and Computation, 16 (8), 4951–4962.
Zhou, G., Lubbers, N., Barros, K., Tretiak, S., & Nebgen, B. (2022). Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics. Proceedings of the National Academy of Sciences, 119 (27), e2120333119. https://doi.org/10.1073/pnas.2120333119
Kulichenko, M., Barros, K., Lubbers, N., Fedik, N., Zhou, G., Tretiak, S., Nebgen, B., & Niklasson, A. M. N. (2023). Semi-empirical shadow molecular dynamics: A PyTorch implementation. Journal of Chemical Theory and Computation, 19 (11), 3209–3222.