On the design space between molecular mechanics and machine learning force fields DOI
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen

et al.

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(2)

Published: April 2, 2025

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

Language: Английский

Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023 DOI Creative Commons
Igor Poltavsky, Anton Charkin-Gorbulin, Mirela Puleva

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Assessing the performance of modern machine learning force fields across diverse chemical systems to identify their strengths and limitations within TEA Challenge 2023.

Language: Английский

Citations

2
On the design space between molecular mechanics and machine learning force fields DOI
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen

et al.

Applied Physics Reviews, Journal Year: 2025, Volume and Issue: 12(2)

Published: April 2, 2025

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

Language: Английский

Citations

0