active learning 2ab initio molecular dynamics 1electrochemistry 1machine learning force fields 1machine learning potentials 1model fine-tuning 1molecular simulations 1spectroscopy 1uncertainty estimation 1workflow automation 1
From the 2 of 2 linked papers with an AI index.
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
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-ph2026
Ai2-Kit: Streamlining AI-Accelerated Ab Initio Workflows for Complex Chemical Systems
Sheng Bi, Wei-Hong Xu, Yong-Bin Zhuang +47
The paper introduces ai2-kit, a software toolkit that streamlines AI‑accelerated ab initio workflows for complex chemical systems by providing command‑line and Python interfaces fo…