relentless: Transparent, reproducible molecular dynamics simulations for optimization
relentless is an open-source Python package that enables the optimization of objective functions computed using molecular dynamics simulations. It has a high-level, extensible interface for model parameterization; setting up, running, and analyzing simulations natively in established software packages; and gradient-based optimization. We describe the design and implementation of relentless in the context of relative entropy minimization, and we demonstrate its abilities to design pairwise interactions between particles that form targeted structures. relentless aims to streamline the development of computational materials design methodologies and promote the transparency and reproducibility of complex workflows integrating molecular dynamics simulations.
Authors
- Adithya N Sreenivasan (ORCID: https://orcid.org/0000-0002-0460-4691)
- Michael P. Howard (ORCID: https://orcid.org/0000-0002-9561-4165)
- Zachary M. Sherman (ORCID: https://orcid.org/0000-0001-9798-287X)
- C. Levi Petix (ORCID: https://orcid.org/0000-0002-0483-7495)
Institutions
- University of Washington (US)
- Massachusetts Institute of Technology (US)
- Auburn University (US)
- The University of Texas at Austin (US)
Publication Details
- Journal
- The Journal of Chemical Physics
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1063/5.0233683
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- National Science Foundation
- U.S. Department of Education
- Welch Foundation
- Arnold and Mabel Beckman Foundation