Recent times, Deep ML models (Specially GNNs) achieved great success towards learning representations of Crystal Materials. This repository contains a collection of resources and papers on Deep Learning Models on Crystal Solid State Materials

• Papers
  • Survey
  • Sequential Models
  • GNN Based models
  • Equivariant Networks
  • Transformer
  • Materials Generation

Recent advances and applications of deep learning methods in materials science
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth Holm, Shyue Ping Ong & Chris Wolverton
npj Computational Materials 2022. [Paper]

Elemnet: Deep learning the chemistry of materials from only elemental composition
Dipendra Jha, Logan Ward, Zijiang Yang, Christopher Wolverton, Ian Foster, Wei-keng Liao,Alok Choudhary,Ankit Agrawal
Scientific Reports 2018. [Paper]

IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
Dipendra Jha, Logan Ward, Zijiang Yang, Christopher Wolverton, Ian Foster, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
KDD 2019. [Paper]

Predicting materials properties without crystal structure: deep representation learning from stoichiometry
Rhys E. A. Goodall, Alpha A. Lee
Nature communications 2020. [Paper]

Compositionally restricted attention-based network for materials property predictions
Anthony Yu-Tung Wang, Steven K. Kauwe, Ryan J. Murdock & Taylor D. Sparks
npj Computational Materials 2021. [Paper]

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller
NeurIPS 2017. [Paper][Github]

(CGCNN) Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
Tian Xie, Jeffrey C. Grossman
Phys. Rev. Lett. 2018. [Paper][Github]

(MEGNet) Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng,Shyue Ping Ong
Chemistry of Materials 2019. [Paper][Github]

(GATGNN) 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, Jianjun Hu
Physical Chemistry Chemical Physics 2020. [Paper][Github]

Crystal graph attention networks for the prediction of stable materials
Jonathan Schmidt, Love Pettersson, Claudio Verdozzi, Silvana Botti,Miguel A. L. Marques
Science Advances 2021. [Paper]

CrysXPP: An explainable property predictor for crystalline materials
Kishalay Das, Bidisha Samanta, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly
npj Computational Materials 2021. [Paper][Github]

(ALIGNN) Atomistic Line Graph Neural Network for improved materials property predictions
Kamal Choudhary, Brian DeCost
npj Computational Materials 2021. [Paper][Github]

(ALIGNN-d) Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy
Tim Hsu, Tuan Anh Pham, Nathan Keilbart, Stephen Weitzner, James Chapman, Penghao Xiao, S. Roger Qiu,Xiao Chen and Brandon C. Wood
npj Computational Materials 2022. [Paper][Github]

CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials.
Kishalay Das, Bidisha Samanta, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly
AAAI 2023 [Paper][Github]

Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction.
Yuchao Lin, Keqiang Yan, Youzhi Luo, Yi Liu, Xiaoning Qian, Shuiwang Ji
ICML 2023 [Paper][Github]

CrysMMNet: Multimodal Representation for Crystal Property Prediction.
Kishalay Das, Pawan Goyal, Seung-Cheol Lee, Satadeep Bhattacharjee, Niloy Ganguly
UAI 2023 [Paper][Github]

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Shengchao Liu, weitao Du, Yanjing Li, Zhuoxinran Li, Zhiling Zheng, Chenru Duan, Zhi-Ming Ma, Omar Yaghi, Animashree Anandkumar, Christian Borgs, Jennifer Chayes, Hongyu Guo, Jian Tang
NeurIPS 2023. [Paper][Github]

A Diffusion-Based Pre-training Framework for Crystal Property Prediction
Zixing Song; Ziqiao Meng; Irwin King
AAAI 2024. [Paper][Github]

(Matformer) Periodic Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Yi Liu, Yuchao Lin, Shuiwang Ji
NeurIPS 2022. [Paper][Github]

Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
Namkyeong Lee, Heewoong Noh, Sungwon Kim, Dongmin Hyun, Gyoung S. Na, Chanyoung Park
NeurIPS 2023. [Paper][Github]

Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Tatsunori Taniai, Ryo Igarashi, Yuta Suzuki, Naoya Chiba, Kotaro Saito, Yoshitaka Ushiku, Kanta Ono
ICLR 2024. [Paper][Github]

Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji
ICLR 2024. [Paper][Github]

Conformal Crystal Graph Transformer with Robust Encoding of Periodic Invariance
Yingheng Wang; Shufeng Kong; John M. Gregoire; Carla P. Gomes
AAAI 2024. [Paper][Github]

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, Boris Kozinsky
Nature Communications 2022. [Paper]

Equivariant Networks for Crystal Structures
Sékou-Oumar Kaba, Siamak Ravanbakhsh
NeurIPS 2022. [Paper]

Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi Jaakkola
ICLR 2021. [Paper][Github]

Towards Symmetry-Aware Generation of Periodic Materials
Youzhi Luo, Chengkai Liu, Shuiwang Ji
NeurIPS 2023. [Paper][Github]

Crystal Structure Prediction by Joint Equivariant Diffusion
Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, Yang Liu
NeurIPS 2023. [Paper][Github]

MatterGen: a generative model for inorganic materials design
Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie
Arxiv. [Paper][Github]

Scalable Diffusion for Materials Generation
Sherry Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, Ekin Dogus Cubuk
ICLR 2024. [Paper][Github]

Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary Ward Ulissi
ICLR 2024. [Paper][Github]

Space Group Constrained Crystal Generation
Rui Jiao, Wenbing Huang, Yu Liu, Deli Zhao, Yang Liu
ICLR 2024. [Paper][Github]

M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery
Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John Gregoire, Carla Gomes
NeurIPS 2023. [Paper][Github]