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.

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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

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article

relentless: Transparent, reproducible molecular dynamics simulations for optimization

Adithya N Sreenivasan, Michael P. Howard, Zachary M. Sherman, C. Levi Petix
2 citations
The Journal of Chemical Physics
Machine Learning in Materials Science
0.23
article

relentless: Transparent, reproducible molecular dynamics simulations for optimization

Adithya N Sreenivasan, Michael P. Howard, Zachary M. Sherman, C. Levi Petix
article en
2 citations

Abstract

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.

The Journal of Chemical PhysicsVol. 161(21)
University of Washington (US), Massachusetts Institute of Technology (US), Auburn University (US), The University of Texas at Austin (US)
National Science Foundation, U.S. Department of Education, Welch Foundation, Arnold and Mabel Beckman Foundation
Openalex Percentile: Top 49%
Machine Learning in Materials Science
0.23
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relentless: Transparent, reproducible molecular dynamics simulations for optimization — Adithya N Sreenivasan, Michael P. Howard, et al. · The Journal of Chemical Physics (2024) | TGRS Research Map | TGRS