58 citations · 147 across the 21 of their papers we have counts for
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cs.LG2023★ 8 cited
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Shengchao Liu, Weitao Du, Yanjing Li +10
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of…
cond-mat.mtrl-sci2023★ 3 cited
MHub: Unlocking the Potential of Machine Learning for Materials Discovery
Yuanqi Du, Yingheng Wang, Yining Huang +6
We introduce MHub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especial…
physics.chem-ph2023★ 4 cited
Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model
Chenru Duan, Yuanqi Du, Haojun Jia +1
Transition state (TS) search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D TS structures, however, requires nu…