8 citations · 19 across the 7 of their papers we have counts for
7 papers
SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles
Gregor N. C. Simm, Jean Hélie, Hannes Schulz +13
Polymers are a versatile class of materials with widespread industrial applications. Advanced computational tools could revolutionize their design, but their complex, multi-scale n…
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields
Shihao Shao, Haoran Geng, Zun Wang +1
Machine Learning Force Fields (MLFFs) are of great importance for chemistry, physics, materials science, and many other related fields. The Clebsch-Gordan Transform (CG transform)…
SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning
Hongfei Wu, Lijun Wu, Guoqing Liu +3
In this paper, we develop SE3Set, an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning. Hypergraphs are not merely an…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
Long-Short-Range Message-Passing: A Physics-Informed Framework to Capture Non-Local Interaction for Scalable Molecular Dynamics Simulation
Yunyang Li, Yusong Wang, Lin Huang +7
Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem…
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022
Yusong Wang, Shaoning Li, Zun Wang +4
In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a gi…
ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules
Yusong Wang, Shaoning Li, Xinheng He +6
Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular p…