3 papers
physics.chem-ph2026
Universal Machine-learning Molecular Dynamics at the Speed of Empirical Potentials
Tiancheng Li, Jianming Xue, Linfeng Zhang +2
No interatomic potential has offered universality across chemistry, near-first-principles accuracy and the speed of empirical potentials at once. Here we introduce DPA4C, an equiva…
physics.chem-ph2026
DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
Tiancheng Li, Wentao Li, Anyang Peng +4
Machine-learning interatomic potentials now approach quantum-mechanical accuracy, but the most expressive equivariant architectures are costly to evaluate, and the leading ones dep…
physics.chem-ph2024
Efficient and Precise Force Field Optimization for Biomolecules Using DPA-2
Junhan Chang, Duo Zhang, Yuqing Deng +5
Molecular simulations are essential tools in computational chemistry, enabling the prediction and understanding of molecular interactions and thermodynamic properties of biomolecul…