Fetching the paper…
Reading the bibliography…
Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science.
Elements of x-ray diffraction
Bernard Dennis Cullity and R Smoluchowski · 1957
Earlier work this paper cites.
The dynamical theory of x-ray diffraction
RW James · 1963
Earlier work this paper cites.
Structure determination by X-ray crystallography , volume 233
Marcus Frederick Charles Ladd, Rex Alfred Palmer, and Rex Alfred Palmer · 1977
Earlier work this paper cites.
Solid state physics
Neil W Ashcroft, N David Mermin, and Sergio Rodriguez · 1978
Earlier work this paper cites.
Solid State Physics
Neil W. Ashcroft and N. David Mermin · 2001
Earlier work this paper cites.
Electrons and phonons: the theory of transport phenomena in solids
John M Ziman · 2001
Earlier work this paper cites.
Regularized multi–task learning
Theodoros Evgeniou and Massimiliano Pontil · 2004
Earlier work this paper cites.
Physical principles of electron microscopy , volume 56
Ray F Egerton et al · 2005
Earlier work this paper cites.
The Physics of Solids
Eleftherios N. Economou · 2010
Earlier work this paper cites.
Fast and accurate modeling of molecular atomization energies with machine learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O Anatole Von Lilienfeld · 2012
Earlier work this paper cites.
Festkörperphysik
Richard Gross and Achim Marx · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma · 2014
Earlier work this paper cites.
Density functional theory: Its origins, rise to prominence, and future
Robert O Jones · 2015
Earlier work this paper cites.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean · 2017
Earlier work this paper cites.
Applying machine learning techniques to predict the properties of energetic materials
Daniel C Elton, Zois Boukouvalas, Mark S Butrico, Mark D Fuge, and Peter W Chung · 2018
Earlier work this paper cites.
Elemnet: Deep learning the chemistry of materials from only elemental composition
Dipendra Jha, Logan Ward, Arindam Paul, Wei-keng Liao, Alok Choudhary, Chris Wolverton, and Ankit Agrawal · 2018
Cited alongside, same era.
Schnet–a deep learning architecture for molecules and materials
Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller · 2018
Cited alongside, same era.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Super-convergence: Very fast training of neural networks using large learning rates
Leslie N Smith and Nicholay Topin · 2019
Cited alongside, same era.
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 JL Billinge, et al · 2022
Later among the works it cites.
Structured multi-task learning for molecular property prediction
Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, and Jian Tang · 2022
Later among the works it cites.
Periodic graph transformers for crystal material property prediction
Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji · 2022
Later among the works it cites.
Capturing long-range interaction with reciprocal space neural network
Hongyu Yu, Liangliang Hong, Shiyou Chen, Xingao Gong, and Hongjun Xiang · 2022
Later among the works it cites.
Towards foundational models for molecular learning on large-scale multi-task datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, et al · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The joint automated repository for various integrated simulations (jarvis) for data-driven materials design
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 · 2020
Cited alongside, same era.
Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain · 2020
Cited alongside, same era.
Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
Cited alongside, same era.
Predicting materials properties without crystal structure: deep representation learning from stoichiometry
Rhys EA Goodall and Alpha A Lee · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Atomistic line graph neural network for improved materials property predictions
Kamal Choudhary and Brian DeCost · 2021
Cited alongside, same era.
Materials property prediction for limited datasets enabled by feature selection and joint learning with modnet
Pierre-Paul De Breuck, Geoffroy Hautier, and Gian-Marco Rignanese · 2021
Cited alongside, same era.
Later among the works it cites.
Unifying molecular and textual representations via multi-task language modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica · 2023
Later among the works it cites.
Ewald-based long-range message passing for molecular graphs
Arthur Kosmala, Johannes Gasteiger, Nicholas Gao, and Stephan Günnemann · 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.
Comprehensive Inorganic Chemistry III
Kenneth R Poeppelmeier · 2023
Later among the works it cites.
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, et al · 2023
Later among the works it cites.
Physical consistency bridges heterogeneous data in molecular multi-task learning
Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, and Tie-Yan Liu · 2024
Later among the works it cites.
Connectivity optimized nested line graph networks for crystal structures
Robin Ruff, Patrick Reiser, Jan Stühmer, and Pascal Friederich · 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.
Direction-oriented multi-objective learning: Simple and provable stochastic algorithms
Peiyao Xiao, Hao Ban, and Kaiyi Ji · 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.
Crystalframer: Rethinking the role of frames for se (3)-invariant crystal structure modeling
Yusei Ito, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku, and Kanta Ono · 2025
Closest in time.