Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations

Gianni De Fabritiis
8/17/2024
physics.chem-phcs.LG

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Abstract

Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simulating complex molecular systems. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields.

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Cite this paper

@article{fabritiis2024machine,
  title  = {Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations},
  author = {Gianni De Fabritiis},
  year   = {2024},
  eprint = {2408.12625},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.12625v1}
}

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