132 citations · 134 across the 3 of their papers we have counts for
4 papers
Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generation
Kaipeng Zeng, Wenxi Zhai, Shengrui Xu +5
Transition-state (TS) structures define the energetic barriers and mechanistic pathways of elementary chemical reactions, yet their identification remains computationally demanding…
A Graph Neural Network for the Era of Large Atomistic Models
Duo Zhang, Anyang Peng, Chun Cai +11
Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functio…
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…
Designing compact training sets for data-driven molecular property prediction
Bowen Li, Srinivas Rangarajan
In this paper, we consider the problem of designing a training set using the most informative molecules from a specified library to build data-driven molecular property models. Spe…