5 papers
Physics-grounded generative design of inherently stable, novel and controllable crystal structures
Zhilong Song, Qionghua Zhou, Chongyi Ling +3
Generative inverse design is reshaping the discovery of functional crystalline materials. Yet current generative models face challenges in simultaneously achieving stability, novel…
Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
Zhilong Song, Zongmin Zhang, Lixue Cheng
Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow…
AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces
Zongmin Zhang, Yuyang Lou, Bowen Zhang +6
Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computatio…
A Survey of Neural Network Variational Monte Carlo from a Computing Workload Characterization Perspective
Zhengze Xiao, Xuanzhe Ding, Yuyang Lou +2
Neural Network Variational Monte Carlo (NNVMC) has emerged as a promising paradigm for solving quantum many-body problems by combining variational Monte Carlo with expressive neura…
An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking
Adam Foster, Zeno Schätzle, P. Bernát Szabó +7
Reliable description of bond breaking remains a major challenge for quantum chemistry due to the multireferential character of the electronic structure in dissociating species. Mul…