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

arXiv: https://arxiv.org/abs/2408.12625 · HTML: https://mlsanity.com/paper/2408.12625/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2024.

## Built on by (most cited first, 2 of 2)

- [On the design space between molecular mechanics and machine learning force fields](https://mlsanity.com/paper/2409.01931/) (2024)
- [QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials](https://mlsanity.com/paper/2501.01811/) (2025)

## Abstract (arXiv)

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.