M $^2$ Hub: Unlocking the Potential of Machine Learning for Materials Discovery

14 June 2023

Papers citing "M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery"

8 / 8 papers shown

Title
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction Yu-Ching Lin Keqiang Yan Youzhi Luo Yi Liu Xiaoning Qian Shuiwang Ji 66 32 0 12 Jun 2023
A Systematic Survey of Chemical Pre-trained Models Jun-Xiong Xia Yanqiao Zhu Yuanqi Du Stan Z.Li AI4CE 52 50 0 29 Oct 2022
Periodic Graph Transformers for Crystal Material Property Prediction Keqiang Yan Yi Liu Yu-Ching Lin Shuiwang Ji AI4TS 85 82 0 23 Sep 2022
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs Yi-Lun Liao Tess E. Smidt 73 211 0 23 Jun 2022
Crystal Diffusion Variational Autoencoder for Periodic Material Generation Tian Xie Xiang Fu O. Ganea Regina Barzilay Tommi Jaakkola DiffM BDL 196 224 0 12 Oct 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 188 1,218 0 08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 213 370 0 20 Oct 2020
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 154 1,748 0 02 Mar 2017