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PreprintCC BY 4.0arXiv

PLUMED-JAX: GPU-native enhanced sampling for atomistic simulations

  • Venkat Kapil
Open at sourcePDFDOICite

Abstract

We present PLUMED-JAX, an AI-assisted adaptation of selected PLUMED collective variables, umbrella sampling, and metadynamics for JAX-based atomistic simulations, developed using gpt-6.1-sol with reasoning effort set to high within the Codex harness. Integration with JAX-MD supports compiled molecular dynamics, batched replicas, walkers sharing a bias, and execution across multiple graphics processing units. We validate the framework against PLUMED and exact free energy benchmarks, and demonstrate applications to butane, silicon crystallization, and lithium transport, with severalfold improvements in computational throughput for both classical forcefields and machine learning interatomic potentials.

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Cite

BibTeXRIS
BibTeX
@misc{kapil2026plumedjax,
  title = {{PLUMED-JAX: GPU-native enhanced sampling for atomistic simulations}},
  author = {Venkat Kapil},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.00483},
  eprint = {2611.00483},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.00483},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - PLUMED-JAX: GPU-native enhanced sampling for atomistic simulations
AU  - Venkat Kapil
PY  - 2026
DA  - 2026/11/01
PB  - arXiv
DO  - 10.48550/arxiv.2611.00483
UR  - https://arxiv.org/abs/2611.00483
AB  - We present PLUMED-JAX, an AI-assisted adaptation of selected PLUMED collective variables, umbrella sampling, and metadynamics for JAX-based atomistic simulations, developed using gpt-6.1-sol with reasoning effort set to high within the Codex harness. Integration with JAX-MD supports compiled molecular dynamics, batched replicas, walkers sharing a bias, and execution across multiple graphics processing units. We validate the framework against PLUMED and exact free energy benchmarks, and demonstrate applications to butane, silicon crystallization, and lithium transport, with severalfold improvements in computational throughput for both classical forcefields and machine learning interatomic potentials.
ER  -