# ML based approaches
@article{lu2018accelerated,
  title={Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning},
  author={Lu, Shuaihua and Zhou, Qionghua and Ouyang, Yixin and Guo, Yilv and Li, Qiang and Wang, Jinlan},
  journal={Nature communications},
  volume={9},
  number={1},
  pages={1--8},
  year={2018},
  publisher={Nature Publishing Group}
}

@article{isayev2017universal,
  title={Universal fragment descriptors for predicting properties of inorganic crystals},
  author={Isayev, Olexandr and Oses, Corey and Toher, Cormac and Gossett, Eric and Curtarolo, Stefano and Tropsha, Alexander},
  journal={Nature communications},
  volume={8},
  number={1},
  pages={1--12},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{im2019identifying,
  title={Identifying Pb-free perovskites for solar cells by machine learning},
  author={Im, Jino and Lee, Seongwon and Ko, Tae-Wook and Kim, Hyun Woo and Hyon, YunKyong and Chang, Hyunju},
  journal={npj Computational Materials},
  volume={5},
  number={1},
  pages={1--8},
  year={2019},
  publisher={Nature Publishing Group}
}

@article{ward2017including,
  title={Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations},
  author={Ward, Logan and Liu, Ruoqian and Krishna, Amar and Hegde, Vinay I and Agrawal, Ankit and Choudhary, Alok and Wolverton, Chris},
  journal={Physical Review B},
  volume={96},
  number={2},
  pages={024104},
  year={2017},
  publisher={APS}
}

@article{de2016statistical,
  title={A statistical learning framework for materials science: application to elastic moduli of k-nary inorganic polycrystalline compounds},
  author={De Jong, Maarten and Chen, Wei and Notestine, Randy and Persson, Kristin and Ceder, Gerbrand and Jain, Anubhav and Asta, Mark and Gamst, Anthony},
  journal={Scientific reports},
  volume={6},
  number={1},
  pages={1--11},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{seko2015prediction,
  title={Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization},
  author={Seko, Atsuto and Togo, Atsushi and Hayashi, Hiroyuki and Tsuda, Koji and Chaput, Laurent and Tanaka, Isao},
  journal={Physical review letters},
  volume={115},
  number={20},
  pages={205901},
  year={2015},
  publisher={APS}
}

@article{pilania2015structure,
  title={Structure classification and melting temperature prediction in octet AB solids via machine learning},
  author={Pilania, Ghanshyam and Gubernatis, James E and Lookman, TJPRB},
  journal={Physical Review B},
  volume={91},
  number={21},
  pages={214302},
  year={2015},
  publisher={APS}
}

@article{seko2017representation,
  title={Representation of compounds for machine-learning prediction of physical properties},
  author={Seko, Atsuto and Hayashi, Hiroyuki and Nakayama, Keita and Takahashi, Akira and Tanaka, Isao},
  journal={Physical Review B},
  volume={95},
  number={14},
  pages={144110},
  year={2017},
  publisher={APS}
}

@article{lee2016prediction,
  title={Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques},
  author={Lee, Joohwi and Seko, Atsuto and Shitara, Kazuki and Nakayama, Keita and Tanaka, Isao},
  journal={Physical Review B},
  volume={93},
  number={11},
  pages={115104},
  year={2016},
  publisher={APS}
}

#GNN Other Domain

# Recommendation System
@article{berg2017graph,
  title={Graph convolutional matrix completion},
  author={Berg, Rianne van den and Kipf, Thomas N and Welling, Max},
  journal={arXiv preprint arXiv:1706.02263},
  year={2017}
}

@inproceedings{ying2018graph,
  title={Graph convolutional neural networks for web-scale recommender systems},
  author={Ying, Rex and He, Ruining and Chen, Kaifeng and Eksombatchai, Pong and Hamilton, William L and Leskovec, Jure},
  booktitle={Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages={974--983},
  year={2018}
}

#Social Networks
@article{hamilton2017inductive,
  title={Inductive representation learning on large graphs},
  author={Hamilton, Will and Ying, Zhitao and Leskovec, Jure},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{chen2018fastgcn,
  title={Fastgcn: fast learning with graph convolutional networks via importance sampling},
  author={Chen, Jie and Ma, Tengfei and Xiao, Cao},
  journal={arXiv preprint arXiv:1801.10247},
  year={2018}
}

@inproceedings{dai2018learning,
  title={Learning steady-states of iterative algorithms over graphs},
  author={Dai, Hanjun and Kozareva, Zornitsa and Dai, Bo and Smola, Alex and Song, Le},
  booktitle={International conference on machine learning},
  pages={1106--1114},
  year={2018},
  organization={PMLR}
}

#chemical Networks

@article{duvenaud2015convolutional,
  title={Convolutional networks on graphs for learning molecular fingerprints},
  author={Duvenaud, David K and Maclaurin, Dougal and Iparraguirre, Jorge and Bombarell, Rafael and Hirzel, Timothy and Aspuru-Guzik, Al{\'a}n and Adams, Ryan P},
  journal={Advances in neural information processing systems},
  volume={28},
  year={2015}
}

@inproceedings{gilmer2017neural,
  title={Neural message passing for quantum chemistry},
  author={Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E},
  booktitle={International conference on machine learning},
  pages={1263--1272},
  year={2017},
  organization={PMLR}
}

#hypernetwork
@article{yadati2019hypergcn,
  title={Hypergcn: A new method for training graph convolutional networks on hypergraphs},
  author={Yadati, Naganand and Nimishakavi, Madhav and Yadav, Prateek and Nitin, Vikram and Louis, Anand and Talukdar, Partha},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@inproceedings{bandyopadhyay2020hypergraph,
  title={Hypergraph attention isomorphism network by learning line graph expansion},
  author={Bandyopadhyay, Sambaran and Das, Kishalay and Murty, M Narasimha},
  booktitle={2020 IEEE International Conference on Big Data (Big Data)},
  pages={669--678},
  year={2020},
  organization={IEEE}
}


# GNN based approaches

@article{schutt2017schnet,
  title={Schnet: A continuous-filter convolutional neural network for modeling quantum interactions},
  author={Sch{\"u}tt, Kristof and Kindermans, Pieter-Jan and Sauceda Felix, Huziel Enoc and Chmiela, Stefan and Tkatchenko, Alexandre and M{\"u}ller, Klaus-Robert},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{xie2018crystal,
	title={Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties},
	author={Xie, Tian and Grossman, Jeffrey C},
	journal={Phys. Rev. Lett.},
	volume={120},
	number={14},
	pages={145301},
	year={2018},
	publisher={APS}
}
	
@article{sanyal2018mt,
	title={MT-CGCNN: Integrating crystal graph convolutional neural network with multitask learning for material property prediction},
	author={Sanyal, Soumya and Balachandran, Janakiraman and Yadati, Naganand and Kumar, Abhishek and Rajagopalan, Padmini and Sanyal, Suchismita and Talukdar, Partha},
	journal={Preprint at https://arxiv.org/abs/1811.05660},
	year={2018}
}

@article{chen2019graph,
  title={Graph networks as a universal machine learning framework for molecules and crystals},
  author={Chen, Chi and Ye, Weike and Zuo, Yunxing and Zheng, Chen and Ong, Shyue Ping},
  journal={Chem. Mater.},
  volume={31},
  number={9},
  pages={3564--3572},
  year={2019},
  publisher={ACS Publications}
}

@article{Wolverton2020,
   title={Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery},
   volume={4},
   ISSN={2475-9953},
   url={http://dx.doi.org/10.1103/PhysRevMaterials.4.063801},
   DOI={10.1103/physrevmaterials.4.063801},
   number={6},
   journal={Physical Review Materials},
   publisher={American Physical Society (APS)},
   author={Park, Cheol Woo and Wolverton, Chris},
   year={2020},
   month={Jun}
}

@article{schmidt2021crystal,
  title={Crystal graph attention networks for the prediction of stable materials},
  author={Schmidt, Jonathan and Pettersson, Love and Verdozzi, Claudio and Botti, Silvana and Marques, Miguel AL},
  journal={Science Advances},
  volume={7},
  number={49},
  pages={eabi7948},
  year={2021},
  publisher={American Association for the Advancement of Science}
}

@article{choudhary2021atomistic,
  title={Atomistic Line Graph Neural Network for improved materials property predictions},
  author={Choudhary, Kamal and DeCost, Brian},
  journal={npj Computational Materials},
  volume={7},
  number={1},
  pages={1--8},
  year={2021},
  publisher={Nature Publishing Group}
}

@article{das2022crysxpp,
  title={CrysXPP: An explainable property predictor for crystalline materials},
  author={Das, Kishalay and Samanta, Bidisha and Goyal, Pawan and Lee, Seung-Cheol and Bhattacharjee, Satadeep and Ganguly, Niloy},
  journal={npj Computational Materials},
  volume={8},
  number={1},
  pages={1--11},
  year={2022},
  publisher={Nature Publishing Group}
}

@article{louis2020graph,
  title={Graph convolutional neural networks with global attention for improved materials property prediction},
  author={Louis, Steph-Yves and Zhao, Yong and Nasiri, Alireza and Wang, Xiran and Song, Yuqi and Liu, Fei and Hu, Jianjun},
  journal={Physical Chemistry Chemical Physics},
  volume={22},
  number={32},
  pages={18141--18148},
  year={2020},
  publisher={Royal Society of Chemistry}
}

@article{hsu2021efficient,
  title={Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy},
  author={Hsu, Tim and Pham, Tuan Anh and Keilbart, Nathan and Weitzner, Stephen and Chapman, James and Xiao, Penghao and Qiu, S Roger and Chen, Xiao and Wood, Brandon C},
  journal={arXiv preprint arXiv:2109.11576},
  year={2021}
}

@inproceedings{yan2022periodic,
  title={Periodic Graph Transformers for Crystal Material Property Prediction},
  author={Keqiang Yan and Yi Liu and Yuchao Lin and Shuiwang Ji},
  booktitle={The 36th Annual Conference on Neural Information Processing Systems},
  year={2022}
}

@article{das2023crysgnn,
  title={CrysGNN: Distilling pre-trained knowledge to enhance property prediction for crystalline materials},
  author={Das, Kishalay and Samanta, Bidisha and Goyal, Pawan and Lee, Seung-Cheol and Bhattacharjee, Satadeep and Ganguly, Niloy},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2023}
}





#CIFID
@article{choudhary2018machine,
  title={Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape},
  author={Choudhary, Kamal and DeCost, Brian and Tavazza, Francesca},
  journal={Physical review materials},
  volume={2},
  number={8},
  pages={083801},
  year={2018},
  publisher={APS}
}


#Elemnet

@article{jha2019enhancing,
  title={Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning},
  author={Jha, Dipendra and Choudhary, Kamal and Tavazza, Francesca and Liao, Wei-keng and Choudhary, Alok and Campbell, Carelyn and Agrawal, Ankit},
  journal={Nature communications},
  volume={10},
  number={1},
  pages={1--12},
  year={2019},
  publisher={Nature Publishing Group}
}

#Robocrystallographer
@article{ganose2019robocrystallographer,
  title={Robocrystallographer: automated crystal structure text descriptions and analysis},
  author={Ganose, Alex M and Jain, Anubhav},
  journal={MRS Communications},
  volume={9},
  number={3},
  pages={874--881},
  year={2019},
  publisher={Cambridge University Press}
}

#CDVAE
@article{xie2021crystal,
  title={Crystal diffusion variational autoencoder for periodic material generation},
  author={Xie, Tian and Fu, Xiang and Ganea, Octavian-Eugen and Barzilay, Regina and Jaakkola, Tommi},
  journal={arXiv preprint arXiv:2110.06197},
  year={2021}
}

@article{dwivedi2020benchmarkgnns,
  title={Benchmarking Graph Neural Networks},
  author={Dwivedi, Vijay Prakash and Joshi, Chaitanya K and Luu, Anh Tuan and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier},
  journal={arXiv preprint arXiv:2003.00982},
  year={2020}
}

#dataset
@article{MP,
  title={Commentary: The Materials Project: A materials genome approach to accelerating materials innovation},
  author={Jain, Anubhav and Ong, Shyue Ping and Hautier, Geoffroy and Chen, Wei and Richards, William Davidson and Dacek, Stephen and Cholia, Shreyas and Gunter, Dan and Skinner, David and Ceder, Gerbrand and others},
  journal={APL materials},
  volume={1},
  number={1},
  pages={011002},
  year={2013},
  publisher={American Institute of PhysicsAIP}
}

@article{choudhary2020joint,
  title={The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design},
  author={Choudhary, Kamal and Garrity, Kevin F and Reid, Andrew CE and DeCost, Brian and Biacchi, Adam J and Hight Walker, Angela R and Trautt, Zachary and Hattrick-Simpers, Jason and Kusne, A Gilad and Centrone, Andrea and others},
  journal={npj computational materials},
  volume={6},
  number={1},
  pages={173},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

#Optimizer
@article{loshchilov2017decoupled,
  title={Decoupled weight decay regularization},
  author={Loshchilov, Ilya and Hutter, Frank},
  journal={arXiv preprint arXiv:1711.05101},
  year={2017}
}

@article{smith2018disciplined,
  title={A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay},
  author={Smith, Leslie N},
  journal={arXiv preprint arXiv:1803.09820},
  year={2018}
}
@article{Larsen,
  title={Definition of a scoring parameter to identify low-dimensional materials components},
  author={Larsen, Peter Mahler and Pandey, Mohnish and Strange, Mikkel and Jacobsen, Karsten Wedel},
  journal={Physical Review Materials},
  volume={3},
  number={3},
  pages={034003},
  year={2019},
  publisher={APS}
}

@inproceedings{Gilmer,
  title={Neural message passing for quantum chemistry},
  author={Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E},
  booktitle={International conference on machine learning},
  pages={1263--1272},
  year={2017},
  organization={PMLR}
}


#domain adaptive [retraining]
@article{gururangan2020don,
  title={Don't stop pretraining: Adapt language models to domains and tasks},
  author={Gururangan, Suchin and Marasovi{\'c}, Ana and Swayamdipta, Swabha and Lo, Kyle and Beltagy, Iz and Downey, Doug and Smith, Noah A},
  journal={arXiv preprint arXiv:2004.10964},
  year={2020}
}
@article{AFLOW,
  title={The AFLOW library of crystallographic prototypes: part 1},
  author={Mehl, Michael J and Hicks, David and Toher, Cormac and Levy, Ohad and Hanson, Robert M and Hart, Gus and Curtarolo, Stefano},
  journal={Computational Materials Science},
  volume={136},
  pages={S1--S828},
  year={2017},
  publisher={Elsevier}
}
@article{pymatgen,
  title={Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis},
  author={Ong, Shyue Ping and Richards, William Davidson and Jain, Anubhav and Hautier, Geoffroy and Kocher, Michael and Cholia, Shreyas and Gunter, Dan and Chevrier, Vincent L and Persson, Kristin A and Ceder, Gerbrand},
  journal={Computational Materials Science},
  volume={68},
  pages={314--319},
  year={2013},
  publisher={Elsevier}
}
@article{spglib,
  title={$\{$Spglib$\}$: a software library for crystal symmetry search},
  author={Togo, Atsushi and Tanaka, Isao},
  journal={arXiv preprint arXiv:1808.01590},
  year={2018}
}


@inproceedings{vig-2019-multiscale,
    title = "A Multiscale Visualization of Attention in the Transformer Model",
    author = "Vig, Jesse",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/P19-3007",
    doi = "10.18653/v1/P19-3007",
    pages = "37--42",
}
