active learning 1machine learning force fields 1model fine-tuning 1molecular simulations 1uncertainty estimation 1
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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
The paper introduces a cheap uncertainty estimator called last-layer-projection regression (LLPR) for active learning, enabling machine‑learning force fields to achieve full‑data a…
physics.chem-ph2024
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…