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

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. Machine learning (ML) potentials are poised to revolutionize molecular simulations across multiple scales, leveraging the innate ability of neural networks to capture complex correlations in high-dimensional spaces. Here, I discuss how these advanced models can dramatically improve the accuracy and efficiency of simulations, from quantum-mechanical calculations to coarse-grained dynamics. By bridging the gap between atomistic detail and macroscopic behavior, ML potentials promise to unlock new insights into molecular processes, drug discovery, and materials design [Duignan24]. I highlight recent successes, current challenges, and future directions in this rapidly evolving field, emphasizing the transformative potential of ML-driven simulations in chemical biology and related disciplines.