9 papers
DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
Tiancheng Li, Wentao Li, Anyang Peng +4
Machine-learning interatomic potentials now approach quantum-mechanical accuracy, but the most expressive equivariant architectures are costly to evaluate, and the leading ones dep…
DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation
Jiangjie Qiu, Yijun Li, Wentao Li +1
Property-conditional molecular generation should produce valid, diverse molecules while responding to continuous target values at low sampling cost. We introduce DriftingMol, a two…
CycleChemist: A Dual-Pronged Machine Learning Framework for Organic Photovoltaic Discovery
Hou Hei Lam, Jiangjie Qiu, Xiuyuan Hu +5
Organic photovoltaic (OPV) materials offer a promising path toward sustainable energy generation, but their development is limited by the difficulty of identifying high performance…
AdsorbFlow: energy-conditioned flow matching enables fast and realistic adsorbate placement
Jiangjie Qiu, Wentao Li, Honghao Chen +2
Identifying low-energy adsorption geometries on catalytic surfaces is a practical bottleneck for computational heterogeneous catalysis: the difficulty lies not only in the cost of…
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…
Bias as a Virtue: Rethinking Generalization under Distribution Shifts
Ruixuan Chen, Wentao Li, Jiahui Xiao +3
Machine learning models often degrade when deployed on data distributions different from their training data. Challenging conventional validation paradigms, we demonstrate that hig…