ab initio molecular dynamics 1active learning 1electrochemistry 1machine learning potentials 1spectroscopy 1workflow automation 1
From the 1 of 3 linked papers with an AI index.
3 papers
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…
physics.chem-ph2026
Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional
Chikashi Shinagawa, So Takamoto, Daiki Shintani +7
Universal Machine Learning Interatomic Potentials (uMLIPs) enable atomistic simulations and high-throughput screening at scales far beyond those accessible with density functional…
physics.chem-ph2025
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng +44
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for m…