132 citations · 166 across the 3 of their papers we have counts for
4 papers
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen +14
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 e…
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…
Observation of dendrite formation at Li metal-electrolyte interface: A machine-learning enhanced constant potential framework
Taiping Hu, Haichao Huang, Guobing Zhou +8
Uncontrollable dendrites growth during electrochemical cycles leads to low Coulombic efficiency and critical safety issues in Li metal batteries. Hence, a comprehensive understandi…
DPA-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…