Deep Residual Learning for Molecular Force Fields

Accurate interatomic interaction modeling via force fields is critical for biological studies and drug discovery, but balancing accuracy and generalizability remains challenging. We introduce ResFF (Residual Learning Force Field), a hybrid machine learning force field that employs deep residual learning to integrate physics-based learnable molecular mechanics covalent terms with residual corrections from a lightweight equivariant neural network. Through a three-stage joint optimization, the two components are trained in a complementary manner to achieve optimal performance. Benchmarks show ResFF outperforms classical and neural network force fields in generalization (mean absolute error (MAE): 1.16 kcal/mol on Gen2-Opt, 0.90 kcal/mol on DES370K), torsional profiles (MAE: 0.45/0.48 kcal/mol on TorsionNet-500 and Torison Scan), and intermolecular interactions (MAE: 0.32 kcal/mol on S66×8). It also enables precise energy minima reproduction and stable molecular dynamics of biological systems. ResFF merges physical constraints with neural expressiveness, offering a robust tool for accurate and efficient molecular simulation.