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

Gianni De Fabritiis

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arXiv · 2408.12625

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

Gianni De Fabritiis

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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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Gianni De Fabritiis. 2024-08-17. Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations. https://arxiv.org/abs/2408.12625

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