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We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond · 2012
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Commentary: 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, et al · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2014
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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
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Big Data Meets Quantum Chemistry Approximations: The Δ \Delta -Machine Learning Approach
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole von Lilienfeld · 2015
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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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
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
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Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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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
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The nomad laboratory: from data sharing to artificial intelligence
Claudia Draxl and Matthias Scheffler · 2019
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Crystal graph neural networks for data mining in materials science
Takenori Yamamoto · 2019
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Optimization of molecules via deep reinforcement learning
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N Zare, and Patrick Riley · 2019
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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
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
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Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
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Multi-objective molecule generation using interpretable substructures
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Reinforcement learning for molecular design guided by quantum mechanics
Gregor Simm, Robert Pinsler, and José Miguel Hernández-Lobato · 2020
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Gradient surgery for multi-task learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn · 2020
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Flow network based generative models for non-iterative diverse candidate generation
Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio · 2021
Sample efficiency matters: a benchmark for practical molecular optimization
Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley · 2022
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A generalist neural algorithmic learner
Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, and Petar Veličković · 2022
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Equivariant networks for crystal structures
Oumar Kaba and Siamak Ravanbakhsh · 2022
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess Smidt · 2022
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Ai4mat: Ai for accelerated materials design neurips 2022 workshop, 2022b
Santiago Miret, Marta Skreta, Benjamin Sanchez-Lengelin, Shyue Ping Ong, Zamyla Morgan-Chan, and Alan Aspuru-Guzik · 2022
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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, et al · 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.
Gemnet: Universal directional graph neural networks for molecules
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
Cited alongside, same era.
Qm7-x, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
Johannes Hoja, Leonardo Medrano Sandonas, Brian G Ernst, Alvaro Vazquez-Mayagoitia, Robert A DiStasio Jr, and Alexandre Tkatchenko · 2021
Cited alongside, same era.
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Cited alongside, same era.
A generalist agent
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-maron, Mai Giménez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas · 2022
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Deep generative model for periodic graphs
Shiyu Wang, Xiaojie Guo, and Liang Zhao · 2022
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Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola · 2022
Later among the works it cites.
PEER: a comprehensive and multi-task benchmark for protein sequence understanding
Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Ma Chang, Runcheng Liu, and Jian Tang · 2022
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Representations of materials for machine learning
James Damewood, Jessica Karaguesian, Jaclyn R Lunger, Aik Rui Tan, Mingrou Xie, Jiayu Peng, and Rafael Gómez-Bombarelli · 2023
Closest in time.
Faenet: Frame averaging equivariant gnn for materials modeling
Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia, Santiago Miret, Fragkiskos D Malliaros, Yoshua Bengio, and David Rolnick · 2023
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Spice, a dataset of drug-like molecules and peptides for training machine learning potentials
Peter Eastman, Pavan Kumar Behara, David L Dotson, Raimondas Galvelis, John E Herr, Josh T Horton, Yuezhi Mao, John D Chodera, Benjamin P Pritchard, Yuanqing Wang, et al · 2023
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Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations
Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi Jaakkola · 2023
Closest in time.
Behavioral cloning for crystal design
Prashant Govindarajan, Santiago Miret, Jarrid Rector-Brooks, Mariano Phielipp, Janarthanan Rajendran, and Sarath Chandar · 2023
Closest in time.
Matsci-nlp: Evaluating scientific language models on materials science language tasks using text-to-schema modeling
Yu Song, Santiago Miret, and Bang Liu · 2023
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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, et al · 2023
Closest in time.
Ml4materials from molecules to materials, 2023
Hanchen Wang, Simon Batzner, Tian Xie, and Xiang Fu · 2023
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