Deep residual learning for molecular force fields
- Xinyu Jiang
- Mingan Chen
- Chuanlong Zeng
- Duanhua Cao
- Jie Yu
- Runze Zhang
- Zunyun Fu
- Zhehuan Fan
- Jiacheng Xiong
- Xutong Li
- Xiaomin Luo
- Dan Teng
- Mingyue Zheng
2026-06-30
Accurate descriptions of interactions between atoms are essential for molecular simulations used to study biology and support drug discovery. Existing force fields often face a trade-off between physical reliability, computational efficiency, and accuracy across unfamiliar molecules. Here we show that Residual Learning Force Field, a hybrid machine learning force field, can reduce this trade-off by combining simple physics-based descriptions of bonded interactions with learned corrections for remaining energetic effects. The two components are trained together through a three-step strategy so that each contributes complementary information. In tests covering drug-like molecules, molecular dimers, torsional energy profiles, energy-minimum structures, and biomolecular simulations, Residual Learning Force Field gives accurate and stable predictions across diverse systems. These results suggest that combining physical constraints with data-driven corrections can provide a practical route toward more reliable and efficient molecular simulation for biological research and drug discovery.