{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-modular-and-differentiable-framework-for","title":"Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations","arxiv_id":"2503.20541","date":"2025-03-26","proceeding":null,"authors":["Henrik Christiansen","Takashi Maruyama","Federico Errica","Viktor Zaverkin","Makoto Takamoto","Francesco Alesiani"],"abstract":"We present an end-to-end differentiable molecular simulation framework (DIMOS) for molecular dynamics and Monte Carlo simulations. DIMOS easily integrates machine-learning-based interatomic potentials and implements classical force fields including particle-mesh Ewald electrostatics. Thanks to its modularity, both classical and machine-learning-based approaches can be easily combined into a hybrid description of the system (ML/MM). By supporting key molecular dynamics features such as efficient neighborlists and constraint algorithms for larger time steps, the framework bridges the gap between hand-optimized simulation engines and the flexibility of a PyTorch implementation. The superior performance and the high versatility is probed in different benchmarks and applications, with speed-up factors of up to $170\\times$. The advantage of differentiability is demonstrated by an end-to-end optimization of the proposal distribution in a Markov Chain Monte Carlo simulation based on Hamiltonian Monte Carlo. Using these optimized simulation parameters a $3\\times$ acceleration is observed in comparison to ad-hoc chosen simulation parameters. The code is available at https://github.com/nec-research/DIMOS.","url_abs":"https://arxiv.org/abs/2503.20541v1","url_pdf":"https://arxiv.org/pdf/2503.20541v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-modular-and-differentiable-framework-for","repo_url":"https://github.com/nec-research/dimos","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}