Fetching the paper…
Reading the bibliography…
Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamental requirement for these networks.
Handbuch der Experimentalphysik
Paul Niggli · 1928
Earlier work this paper cites.
Determination of reduced cells
A. Santoro and A. D. Mighell · 1970
Earlier work this paper cites.
The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, and Kristin A. Persson · 2013
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Scott Kirklin, James E. Saal, Bryce Meredig, Alex Thompson, Jeff W. Doak, Muratahan Aykol, Stephan Rühl, and Chris Wolverton · 2015
Earlier work this paper cites.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
Earlier work this paper cites.
Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Earlier work this paper cites.
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson · 2018
Earlier work this paper cites.
SchNet – A deep learning architecture for molecules and materials
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller · 2018
Earlier work this paper cites.
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Earlier work this paper cites.
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
Tian Xie and Jeffrey C. Grossman · 2018
Earlier work this paper cites.
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong · 2019
Earlier work this paper cites.
Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
Kamal Choudhary, Kevin F. Garrity, Andrew C. E. Reid, Brian DeCost, Adam J. Biacchi, Angela R. Hight Walker, Zachary Trautt, Jason Hattrick-Simpers, A. Gilad Kusne, Andrea Centrone, Albert Davydov, Jie Jiang, Ruth Pachter, Gowoon Cheon, Evan Reed, Ankit Agrawal, Xiaofeng Qian, Vinit Sharma, Houlong Zhuang, Sergei V. Kalinin, Bobby G. Sumpter, Ghanshyam Pilania, Pinar Acar, Subhasish Mandal, Kristjan Haule, David Vanderbilt, Karin Rabe, and Francesca Tavazza · 2020
Earlier work this paper cites.
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
Earlier work this paper cites.
Improving Transformer Optimization Through Better Initialization
Xiao Shi Huang, Felipe Perez, Jimmy Ba, and Maksims Volkovs · 2020
Earlier work this paper cites.
Graph convolutional neural networks with global attention for improved materials property prediction
Steph-Yves Louis, Yong Zhao, Alireza Nasiri, Xiran Wang, Yuqi Song, Fei Liu, and Jianjun Hu · 2020
Earlier work this paper cites.
Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery
Cheol Woo Park and Chris Wolverton · 2020
Cited alongside, same era.
Open Catalyst 2020 (OC20) Dataset and Community Challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi · 2021
Cited alongside, same era.
A geometric-information-enhanced crystal graph network for predicting properties of materials
Jiucheng Cheng, Chunkai Zhang, and Lifeng Dong · 2021
Cited alongside, same era.
Atomistic Line Graph Neural Network for improved materials property predictions
Kamal Choudhary and Brian DeCost · 2021
Cited alongside, same era.
Do Transformers Really Perform Badly for Graph Representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
Cited alongside, same era.
Crystal Structure Prediction by Joint Equivariant Diffusion
Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, and Yang Liu · 2023
Later among the works it cites.
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Yi-Lun Liao and Tess Smidt · 2023
Later among the works it cites.
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, and Shuiwang Ji · 2023
Later among the works it cites.
Smooth, exact rotational symmetrization for deep learning on point clouds
Sergey Pozdnyakov and Michele Ceriotti · 2023
Later among the works it cites.
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets, 2023
Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, and Tie-Yan Liu · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky · 2022
Cited alongside, same era.
Geometric and Physical Quantities improve E(3) Equivariant Message Passing
Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, and Max Welling · 2022
Cited alongside, same era.
A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
Cited alongside, same era.
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, and Tie-Yan Liu · 2022
Cited alongside, same era.
A survey of transformers
Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu · 2022
Cited alongside, same era.
Incompleteness of graph neural networks for points clouds in three dimensions
Sergey N Pozdnyakov and Michele Ceriotti · 2022
Cited alongside, same era.
Frame Averaging for Invariant and Equivariant Network Design
Omri Puny, Matan Atzmon, Edward J. Smith, Ishan Misra, Aditya Grover, Heli Ben-Hamu, and Yaron Lipman · 2022
Cited alongside, same era.
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M. Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Félix Therrien, Jehad Abed, Oleksandr Voznyy, Edward H. Sargent, Zachary Ulissi, and C. Lawrence Zitnick · 2023
Later among the works it cites.
Geometric Transformer with Interatomic Positional Encoding
Yusong Wang, Shaoning Li, Tong Wang, Bin Shao, Nanning Zheng, and Tie-Yan Liu · 2023
Later among the works it cites.
EGraFFBench: evaluation of equivariant graph neural network force fields for atomistic simulations
Vaibhav Bihani, Sajid Mannan, Utkarsh Pratiush, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M. Smedskjaer, Sayan Ranu, and N. M. Anoop Krishnan · 2024
Later among the works it cites.
A Hitchhiker’s Guide to Geometric GNNs for 3D Atomic Systems, 2024
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, and Michael Bronstein · 2024
Later among the works it cites.
Equivariant Frames and the Impossibility of Continuous Canonicalization
Nadav Dym, Hannah Lawrence, and Jonathan W. Siegel · 2024
Later among the works it cites.
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications, 2024
Jiaqi Han, Jiacheng Cen, Liming Wu, Zongzhao Li, Xiangzhe Kong, Rui Jiao, Ziyang Yu, Tingyang Xu, Fandi Wu, Zihe Wang, Hongteng Xu, Zhewei Wei, Yang Liu, Yu Rong, and Wenbing Huang · 2024
Later among the works it cites.
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Yi-Lun Liao, Brandon M Wood, Abhishek Das, and Tess Smidt · 2024
Later among the works it cites.
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency
Yuchao Lin, Jacob Helwig, Shurui Gui, and Shuiwang Ji · 2024
Later among the works it cites.
Gradformer: Graph Transformer with Exponential Decay
Chuang Liu, Zelin Yao, Yibing Zhan, Xueqi Ma, Shirui Pan, and Wenbin Hu · 2024
Later among the works it cites.
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Tatsunori Taniai, Ryo Igarashi, Yuta Suzuki, Naoya Chiba, Kotaro Saito, Yoshitaka Ushiku, and Kanta Ono · 2024
Later among the works it cites.
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, and Shuiwang Ji · 2024
Later among the works it cites.
Contrastive Language-Structure Pre-training Driven by Materials Science Literature, 2025
Yuta Suzuki, Tatsunori Taniai, Ryo Igarashi, Kotaro Saito, Naoya Chiba, Yoshitaka Ushiku, and Kanta Ono · 2025
Closest in time.
Self-Attention with Relative Position Representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2074
Closest in time.