On the design space between molecular mechanics and machine learning force fields

TL;DR

This paper reviews the design space between molecular mechanics and machine learning force fields, emphasizing the speed-accuracy tradeoff, recent advancements, and future directions for more efficient and accurate biomolecular simulations.

Contribution

It provides a comprehensive overview of the current state, challenges, and future prospects of force field development bridging MM and ML approaches.

Findings

ML force fields surpass chemical accuracy but are slower than MM

Speed remains the main bottleneck for ML force fields

Future designs may focus on balancing speed and accuracy

Abstract

A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists -- a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor towards this direction, where differentiable neural functions are parametrized to fit ab initio energies, and furthermore forces through automatic differentiation. We argue that, as of now, the utility of the MLFF models is no longer bottlenecked by accuracy but primarily by their speed (as well as stability and generalizability), as many recent variants, on limited chemical spaces, have long surpassed the chemical accuracy of $1$ kcal/mol -- the empirical threshold beyond which realistic chemical predictions are possible -- though still magnitudes slower than MM. Hoping to kindle explorations and designs of faster, albeit perhaps slightly less accurate MLFFs, in this review, we focus our attention on the design space (the speed-accuracy tradeoff) between MM and ML force fields. After a brief review of the building blocks of force fields of either kind, we discuss the desired properties and challenges now faced by the force field development community, survey the efforts to make MM force fields more accurate and ML force fields faster, envision what the next generation of MLFF might look like.