Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
arXiv:2408.12625
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.
14 pages