132 citations · 132 across the 2 of their papers we have counts for
2 papers
physics.chem-ph2026
Full-data accuracy with fewer labels for training and fine-tuning machine-learning force fields
Sheng Bi, Yi-Ze Wang, Jun Cheng
Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-f…
physics.chem-ph2023★ 132 cited
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…