\begin{thebibliography}{41}
\providecommand{\natexlab}[1]{#1}
\providecommand{\url}[1]{\texttt{#1}}
\expandafter\ifx\csname urlstyle\endcsname\relax
  \providecommand{\doi}[1]{doi: #1}\else
  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi

\bibitem[Bandyopadhyay et~al.(2020)Bandyopadhyay, Das, and
  Murty]{bandyopadhyay2020hypergraph}
Sambaran Bandyopadhyay, Kishalay Das, and M~Narasimha Murty.
\newblock Hypergraph attention isomorphism network by learning line graph
  expansion.
\newblock In \emph{2020 IEEE International Conference on Big Data (Big Data)},
  pages 669--678. IEEE, 2020.

\bibitem[Berg et~al.(2017)Berg, Kipf, and Welling]{berg2017graph}
Rianne van~den Berg, Thomas~N Kipf, and Max Welling.
\newblock Graph convolutional matrix completion.
\newblock \emph{arXiv preprint arXiv:1706.02263}, 2017.

\bibitem[Chen et~al.(2019)Chen, Ye, Zuo, Zheng, and Ong]{chen2019graph}
Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue~Ping Ong.
\newblock Graph networks as a universal machine learning framework for
  molecules and crystals.
\newblock \emph{Chem. Mater.}, 31\penalty0 (9):\penalty0 3564--3572, 2019.

\bibitem[Chen et~al.(2018)Chen, Ma, and Xiao]{chen2018fastgcn}
Jie Chen, Tengfei Ma, and Cao Xiao.
\newblock Fastgcn: fast learning with graph convolutional networks via
  importance sampling.
\newblock \emph{arXiv preprint arXiv:1801.10247}, 2018.

\bibitem[Choudhary and DeCost(2021)]{choudhary2021atomistic}
Kamal Choudhary and Brian DeCost.
\newblock Atomistic line graph neural network for improved materials property
  predictions.
\newblock \emph{npj Computational Materials}, 7\penalty0 (1):\penalty0 1--8,
  2021.

\bibitem[Choudhary et~al.(2018)Choudhary, DeCost, and
  Tavazza]{choudhary2018machine}
Kamal Choudhary, Brian DeCost, and Francesca Tavazza.
\newblock Machine learning with force-field-inspired descriptors for materials:
  Fast screening and mapping energy landscape.
\newblock \emph{Physical review materials}, 2\penalty0 (8):\penalty0 083801,
  2018.

\bibitem[Choudhary et~al.(2020)Choudhary, Garrity, Reid, DeCost, Biacchi,
  Hight~Walker, Trautt, Hattrick-Simpers, Kusne, Centrone,
  et~al.]{choudhary2020joint}
Kamal Choudhary, Kevin~F Garrity, Andrew~CE Reid, Brian DeCost, Adam~J Biacchi,
  Angela~R Hight~Walker, Zachary Trautt, Jason Hattrick-Simpers, A~Gilad Kusne,
  Andrea Centrone, et~al.
\newblock The joint automated repository for various integrated simulations
  (jarvis) for data-driven materials design.
\newblock \emph{npj computational materials}, 6\penalty0 (1):\penalty0 173,
  2020.

\bibitem[Dai et~al.(2018)Dai, Kozareva, Dai, Smola, and Song]{dai2018learning}
Hanjun Dai, Zornitsa Kozareva, Bo~Dai, Alex Smola, and Le~Song.
\newblock Learning steady-states of iterative algorithms over graphs.
\newblock In \emph{International conference on machine learning}, pages
  1106--1114. PMLR, 2018.

\bibitem[Das et~al.(2022)Das, Samanta, Goyal, Lee, Bhattacharjee, and
  Ganguly]{das2022crysxpp}
Kishalay Das, Bidisha Samanta, Pawan Goyal, Seung-Cheol Lee, Satadeep
  Bhattacharjee, and Niloy Ganguly.
\newblock Crysxpp: An explainable property predictor for crystalline materials.
\newblock \emph{npj Computational Materials}, 8\penalty0 (1):\penalty0 1--11,
  2022.

\bibitem[Das et~al.(2023)Das, Samanta, Goyal, Lee, Bhattacharjee, and
  Ganguly]{das2023crysgnn}
Kishalay Das, Bidisha Samanta, Pawan Goyal, Seung-Cheol Lee, Satadeep
  Bhattacharjee, and Niloy Ganguly.
\newblock Crysgnn: Distilling pre-trained knowledge to enhance property
  prediction for crystalline materials.
\newblock \emph{Proceedings of the AAAI Conference on Artificial Intelligence},
  2023.

\bibitem[De~Jong et~al.(2016)De~Jong, Chen, Notestine, Persson, Ceder, Jain,
  Asta, and Gamst]{de2016statistical}
Maarten De~Jong, Wei Chen, Randy Notestine, Kristin Persson, Gerbrand Ceder,
  Anubhav Jain, Mark Asta, and Anthony Gamst.
\newblock A statistical learning framework for materials science: application
  to elastic moduli of k-nary inorganic polycrystalline compounds.
\newblock \emph{Scientific reports}, 6\penalty0 (1):\penalty0 1--11, 2016.

\bibitem[Duvenaud et~al.(2015)Duvenaud, Maclaurin, Iparraguirre, Bombarell,
  Hirzel, Aspuru-Guzik, and Adams]{duvenaud2015convolutional}
David~K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell,
  Timothy Hirzel, Al{\'a}n Aspuru-Guzik, and Ryan~P Adams.
\newblock Convolutional networks on graphs for learning molecular fingerprints.
\newblock \emph{Advances in neural information processing systems}, 28, 2015.

\bibitem[Dwivedi et~al.(2020)Dwivedi, Joshi, Luu, Laurent, Bengio, and
  Bresson]{dwivedi2020benchmarkgnns}
Vijay~Prakash Dwivedi, Chaitanya~K Joshi, Anh~Tuan Luu, Thomas Laurent, Yoshua
  Bengio, and Xavier Bresson.
\newblock Benchmarking graph neural networks.
\newblock \emph{arXiv preprint arXiv:2003.00982}, 2020.

\bibitem[Ganose and Jain(2019)]{ganose2019robocrystallographer}
Alex~M Ganose and Anubhav Jain.
\newblock Robocrystallographer: automated crystal structure text descriptions
  and analysis.
\newblock \emph{MRS Communications}, 9\penalty0 (3):\penalty0 874--881, 2019.

\bibitem[Gilmer et~al.(2017)Gilmer, Schoenholz, Riley, Vinyals, and
  Dahl]{gilmer2017neural}
Justin Gilmer, Samuel~S Schoenholz, Patrick~F Riley, Oriol Vinyals, and
  George~E Dahl.
\newblock Neural message passing for quantum chemistry.
\newblock In \emph{International conference on machine learning}, pages
  1263--1272. PMLR, 2017.

\bibitem[Gururangan et~al.(2020)Gururangan, Marasovi{\'c}, Swayamdipta, Lo,
  Beltagy, Downey, and Smith]{gururangan2020don}
Suchin Gururangan, Ana Marasovi{\'c}, Swabha Swayamdipta, Kyle Lo, Iz~Beltagy,
  Doug Downey, and Noah~A Smith.
\newblock Don't stop pretraining: Adapt language models to domains and tasks.
\newblock \emph{arXiv preprint arXiv:2004.10964}, 2020.

\bibitem[Hamilton et~al.(2017)Hamilton, Ying, and
  Leskovec]{hamilton2017inductive}
Will Hamilton, Zhitao Ying, and Jure Leskovec.
\newblock Inductive representation learning on large graphs.
\newblock \emph{Advances in neural information processing systems}, 30, 2017.

\bibitem[Hsu et~al.(2021)Hsu, Pham, Keilbart, Weitzner, Chapman, Xiao, Qiu,
  Chen, and Wood]{hsu2021efficient}
Tim Hsu, Tuan~Anh Pham, Nathan Keilbart, Stephen Weitzner, James Chapman,
  Penghao Xiao, S~Roger Qiu, Xiao Chen, and Brandon~C Wood.
\newblock Efficient, interpretable graph neural network representation for
  angle-dependent properties and its application to optical spectroscopy.
\newblock \emph{arXiv preprint arXiv:2109.11576}, 2021.

\bibitem[Im et~al.(2019)Im, Lee, Ko, Kim, Hyon, and Chang]{im2019identifying}
Jino Im, Seongwon Lee, Tae-Wook Ko, Hyun~Woo Kim, YunKyong Hyon, and Hyunju
  Chang.
\newblock Identifying pb-free perovskites for solar cells by machine learning.
\newblock \emph{npj Computational Materials}, 5\penalty0 (1):\penalty0 1--8,
  2019.

\bibitem[Isayev et~al.(2017)Isayev, Oses, Toher, Gossett, Curtarolo, and
  Tropsha]{isayev2017universal}
Olexandr Isayev, Corey Oses, Cormac Toher, Eric Gossett, Stefano Curtarolo, and
  Alexander Tropsha.
\newblock Universal fragment descriptors for predicting properties of inorganic
  crystals.
\newblock \emph{Nature communications}, 8\penalty0 (1):\penalty0 1--12, 2017.

\bibitem[Jain et~al.(2013)Jain, Ong, Hautier, Chen, Richards, Dacek, Cholia,
  Gunter, Skinner, Ceder, et~al.]{MP}
Anubhav Jain, Shyue~Ping Ong, Geoffroy Hautier, Wei Chen, William~Davidson
  Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand
  Ceder, et~al.
\newblock Commentary: The materials project: A materials genome approach to
  accelerating materials innovation.
\newblock \emph{APL materials}, 1\penalty0 (1):\penalty0 011002, 2013.

\bibitem[Jha et~al.(2019)Jha, Choudhary, Tavazza, Liao, Choudhary, Campbell,
  and Agrawal]{jha2019enhancing}
Dipendra Jha, Kamal Choudhary, Francesca Tavazza, Wei-keng Liao, Alok
  Choudhary, Carelyn Campbell, and Ankit Agrawal.
\newblock Enhancing materials property prediction by leveraging computational
  and experimental data using deep transfer learning.
\newblock \emph{Nature communications}, 10\penalty0 (1):\penalty0 1--12, 2019.

\bibitem[Larsen et~al.(2019)Larsen, Pandey, Strange, and Jacobsen]{Larsen}
Peter~Mahler Larsen, Mohnish Pandey, Mikkel Strange, and Karsten~Wedel
  Jacobsen.
\newblock Definition of a scoring parameter to identify low-dimensional
  materials components.
\newblock \emph{Physical Review Materials}, 3\penalty0 (3):\penalty0 034003,
  2019.

\bibitem[Lee et~al.(2016)Lee, Seko, Shitara, Nakayama, and
  Tanaka]{lee2016prediction}
Joohwi Lee, Atsuto Seko, Kazuki Shitara, Keita Nakayama, and Isao Tanaka.
\newblock Prediction model of band gap for inorganic compounds by combination
  of density functional theory calculations and machine learning techniques.
\newblock \emph{Physical Review B}, 93\penalty0 (11):\penalty0 115104, 2016.

\bibitem[Loshchilov and Hutter(2017)]{loshchilov2017decoupled}
Ilya Loshchilov and Frank Hutter.
\newblock Decoupled weight decay regularization.
\newblock \emph{arXiv preprint arXiv:1711.05101}, 2017.

\bibitem[Louis et~al.(2020)Louis, Zhao, Nasiri, Wang, Song, Liu, and
  Hu]{louis2020graph}
Steph-Yves Louis, Yong Zhao, Alireza Nasiri, Xiran Wang, Yuqi Song, Fei Liu,
  and Jianjun Hu.
\newblock Graph convolutional neural networks with global attention for
  improved materials property prediction.
\newblock \emph{Physical Chemistry Chemical Physics}, 22\penalty0
  (32):\penalty0 18141--18148, 2020.

\bibitem[Lu et~al.(2018)Lu, Zhou, Ouyang, Guo, Li, and Wang]{lu2018accelerated}
Shuaihua Lu, Qionghua Zhou, Yixin Ouyang, Yilv Guo, Qiang Li, and Jinlan Wang.
\newblock Accelerated discovery of stable lead-free hybrid organic-inorganic
  perovskites via machine learning.
\newblock \emph{Nature communications}, 9\penalty0 (1):\penalty0 1--8, 2018.

\bibitem[Park and Wolverton(2020)]{Wolverton2020}
Cheol~Woo Park and Chris Wolverton.
\newblock Developing an improved crystal graph convolutional neural network
  framework for accelerated materials discovery.
\newblock \emph{Physical Review Materials}, 4\penalty0 (6), Jun 2020.
\newblock ISSN 2475-9953.
\newblock \doi{10.1103/physrevmaterials.4.063801}.
\newblock URL \url{http://dx.doi.org/10.1103/PhysRevMaterials.4.063801}.

\bibitem[Pilania et~al.(2015)Pilania, Gubernatis, and
  Lookman]{pilania2015structure}
Ghanshyam Pilania, James~E Gubernatis, and TJPRB Lookman.
\newblock Structure classification and melting temperature prediction in octet
  ab solids via machine learning.
\newblock \emph{Physical Review B}, 91\penalty0 (21):\penalty0 214302, 2015.

\bibitem[Schmidt et~al.(2021)Schmidt, Pettersson, Verdozzi, Botti, and
  Marques]{schmidt2021crystal}
Jonathan Schmidt, Love Pettersson, Claudio Verdozzi, Silvana Botti, and
  Miguel~AL Marques.
\newblock Crystal graph attention networks for the prediction of stable
  materials.
\newblock \emph{Science Advances}, 7\penalty0 (49):\penalty0 eabi7948, 2021.

\bibitem[Sch{\"u}tt et~al.(2017)Sch{\"u}tt, Kindermans, Sauceda~Felix, Chmiela,
  Tkatchenko, and M{\"u}ller]{schutt2017schnet}
Kristof Sch{\"u}tt, Pieter-Jan Kindermans, Huziel~Enoc Sauceda~Felix, Stefan
  Chmiela, Alexandre Tkatchenko, and Klaus-Robert M{\"u}ller.
\newblock Schnet: A continuous-filter convolutional neural network for modeling
  quantum interactions.
\newblock \emph{Advances in neural information processing systems}, 30, 2017.

\bibitem[Seko et~al.(2015)Seko, Togo, Hayashi, Tsuda, Chaput, and
  Tanaka]{seko2015prediction}
Atsuto Seko, Atsushi Togo, Hiroyuki Hayashi, Koji Tsuda, Laurent Chaput, and
  Isao Tanaka.
\newblock Prediction of low-thermal-conductivity compounds with
  first-principles anharmonic lattice-dynamics calculations and bayesian
  optimization.
\newblock \emph{Physical review letters}, 115\penalty0 (20):\penalty0 205901,
  2015.

\bibitem[Seko et~al.(2017)Seko, Hayashi, Nakayama, Takahashi, and
  Tanaka]{seko2017representation}
Atsuto Seko, Hiroyuki Hayashi, Keita Nakayama, Akira Takahashi, and Isao
  Tanaka.
\newblock Representation of compounds for machine-learning prediction of
  physical properties.
\newblock \emph{Physical Review B}, 95\penalty0 (14):\penalty0 144110, 2017.

\bibitem[Smith(2018)]{smith2018disciplined}
Leslie~N Smith.
\newblock A disciplined approach to neural network hyper-parameters: Part
  1--learning rate, batch size, momentum, and weight decay.
\newblock \emph{arXiv preprint arXiv:1803.09820}, 2018.

\bibitem[Vig(2019)]{vig-2019-multiscale}
Jesse Vig.
\newblock A multiscale visualization of attention in the transformer model.
\newblock In \emph{Proceedings of the 57th Annual Meeting of the Association
  for Computational Linguistics: System Demonstrations}, pages 37--42,
  Florence, Italy, July 2019. Association for Computational Linguistics.
\newblock \doi{10.18653/v1/P19-3007}.
\newblock URL \url{https://www.aclweb.org/anthology/P19-3007}.

\bibitem[Ward et~al.(2017)Ward, Liu, Krishna, Hegde, Agrawal, Choudhary, and
  Wolverton]{ward2017including}
Logan Ward, Ruoqian Liu, Amar Krishna, Vinay~I Hegde, Ankit Agrawal, Alok
  Choudhary, and Chris Wolverton.
\newblock Including crystal structure attributes in machine learning models of
  formation energies via voronoi tessellations.
\newblock \emph{Physical Review B}, 96\penalty0 (2):\penalty0 024104, 2017.

\bibitem[Xie and Grossman(2018)]{xie2018crystal}
Tian Xie and Jeffrey~C Grossman.
\newblock Crystal graph convolutional neural networks for an accurate and
  interpretable prediction of material properties.
\newblock \emph{Phys. Rev. Lett.}, 120\penalty0 (14):\penalty0 145301, 2018.

\bibitem[Xie et~al.(2021)Xie, Fu, Ganea, Barzilay, and
  Jaakkola]{xie2021crystal}
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola.
\newblock Crystal diffusion variational autoencoder for periodic material
  generation.
\newblock \emph{arXiv preprint arXiv:2110.06197}, 2021.

\bibitem[Yadati et~al.(2019)Yadati, Nimishakavi, Yadav, Nitin, Louis, and
  Talukdar]{yadati2019hypergcn}
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis,
  and Partha Talukdar.
\newblock Hypergcn: A new method for training graph convolutional networks on
  hypergraphs.
\newblock \emph{Advances in neural information processing systems}, 32, 2019.

\bibitem[Yan et~al.(2022)Yan, Liu, Lin, and Ji]{yan2022periodic}
Keqiang Yan, Yi~Liu, Yuchao Lin, and Shuiwang Ji.
\newblock Periodic graph transformers for crystal material property prediction.
\newblock In \emph{The 36th Annual Conference on Neural Information Processing
  Systems}, 2022.

\bibitem[Ying et~al.(2018)Ying, He, Chen, Eksombatchai, Hamilton, and
  Leskovec]{ying2018graph}
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William~L Hamilton, and
  Jure Leskovec.
\newblock Graph convolutional neural networks for web-scale recommender
  systems.
\newblock In \emph{Proceedings of the 24th ACM SIGKDD international conference
  on knowledge discovery \& data mining}, pages 974--983, 2018.

\end{thebibliography}
