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The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware.
Ab initio molecular-dynamics simulation of the liquid-metal–amorphous-semiconductor transition in germanium
Georg Kresse and Jürgen Hafner · 1994
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Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Georg Kresse and Jürgen Furthmüller · 1996
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Generalized gradient approximation made simple
John P Perdew, Kieron Burke, and Matthias Ernzerhof · 1996
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Identification of cathode materials for lithium batteries guided by first-principles calculations
G. Ceder, Y. M. Chiang, D. R. Sadoway, M. K. Aydinol, Y. I. Jang, and B. Huang · 1998
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Restoring the density-gradient expansion for exchange in solids and surfaces
John P Perdew, Adrienn Ruzsinszky, Gábor I Csonka, Oleg A Vydrov, Gustavo E Scuseria, Lucian A Constantin, Xiaolan Zhou, and Kieron Burke · 2008
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Computational materials science and chemistry: Accelerating discovery and innovation through simulation-based engineering and science
George Crabtree, Sharon Glotzer, Bill McCurdy, and Jim Roberto · 2010
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The Materials Project: A materials genome approach to accelerating materials innovation
A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, and K. A. Persson · 2013
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, et al · 2013
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Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent L Chevrier, Kristin A Persson, and Gerbrand Ceder · 2013
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Progress in material design for biomedical applications
Mark W. Tibbitt, Christopher B. Rodell, Jason A. Burdick, and Kristi S. Anseth · 2015
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Strongly constrained and appropriately normed semilocal density functional
Jianwei Sun, Adrienn Ruzsinszky, and John P Perdew · 2015
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The thermodynamic scale of inorganic crystalline metastability
Wenhao Sun, Stephen T Dacek, Shyue Ping Ong, Geoffroy Hautier, Anubhav Jain, William D Richards, Anthony C Gamst, Kristin A Persson, and Gerbrand Ceder · 2016
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The aflow library of crystallographic prototypes: part 1
Michael J Mehl, David Hicks, Cormac Toher, Ohad Levy, Robert M Hanson, Gus Hart, and Stefano Curtarolo · 2017
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Robust and synthesizable photocatalysts for co2 reduction: a data-driven materials discovery
Arunima K Singh, Joseph H Montoya, John M Gregoire, and Kristin A Persson · 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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An introduction to electrocatalyst design using machine learning for renewable energy storage
C Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Riviere, et al · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W. Battaglia · 2020
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The ani-1ccx and ani-1x data sets, coupled-cluster and density functional theory properties for molecules
Justin S Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrian E Roitberg, Olexandr Isayev, and Sergei Tretiak · 2020
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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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Accurate and numerically efficient r2scan meta-generalized gradient approximation
James W Furness, Aaron D Kaplan, Jinliang Ning, John P Perdew, and Jianwei Sun · 2020
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A critical examination of compound stability predictions from machine-learned formation energies
Christopher J Bartel, Amalie Trewartha, Qi Wang, Alexander Dunn, Anubhav Jain, and Gerbrand Ceder · 2020
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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, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi · 2021
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Accelerated discovery of 3d printing materials using data-driven multiobjective optimization
Timothy Erps, Michael Foshey, Mina Konaković Luković, Wan Shou, Hanns Hagen Goetzke, Herve Dietsch, Klaus Stoll, Bernhard von Vacano, and Wojciech Matusik · 2021
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High-throughput computational materials screening and discovery of optoelectronic semiconductors
Shulin Luo, Tianshu Li, Xinjiang Wang, Muhammad Faizan, and Lijun Zhang · 2021
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Atomistic line graph neural network for improved materials property predictions
Kamal Choudhary and Brian DeCost · 2021
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Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Daniel Schwalbe-Koda, Aik Rui Tan, and Rafael Gómez-Bombarelli · 2021
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Predicting stable crystalline compounds using chemical similarity
Hai-Chen Wang, Silvana Botti, and Miguel AL Marques · 2021
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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
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Matbench discovery–an evaluation framework for machine learning crystal stability prediction
Janosh Riebesell, Rhys EA Goodall, Anubhav Jain, Philipp Benner, Kristin A Persson, and Alpha A Lee · 2023
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Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
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Exploiting redundancy in large materials datasets for efficient machine learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary, Brian DeCost, Michael Greenwood, and Jason Hattrick-Simpers · 2023
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Issue #2968: Add support for new crystal structure prediction method
Materials Project · 2023
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Issue #3016: Add support for new crystal structure prediction method
Materials Project · 2023
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A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
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MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Ilyes Batatia, David Peter Kovacs, Gregor N. C. Simm, Christoph Ortner, and Gabor Csanyi · 2022
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The design space of e (3)-equivariant atom-centered interatomic potentials
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor NC Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, and Gábor Csányi · 2022
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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, and Boris Kozinsky · 2022
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Gemnet-OC: Developing graph neural networks for large and diverse molecular simulation datasets
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ward Ulissi, C. Lawrence Zitnick, and Abhishek Das · 2022
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Rapid discovery of stable materials by coordinate-free coarse graining
Rhys EA Goodall, Abhijith S Parackal, Felix A Faber, Rickard Armiento, and Alpha A Lee · 2022
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Review of computational approaches to predict the thermodynamic stability of inorganic solids
Christopher J Bartel · 2022
Cited alongside, same era.
Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M Elena, Dávid P Kovács, Janosh Riebesell, Xavier R Advincula, Mark Asta, William J Baldwin, Noam Bernstein, et al · 2023
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Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
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A hitchhiker’s guide to geometric gnns for 3d atomic systems
Alexandre Duval, Simon V Mathis, Chaitanya K Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, and Michael Bronstein · 2023
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Matthew Horton, Jimmy-Xuan Shen, Jordan Burns, Orion Cohen, François Chabbey, Alex M Ganose, Rishabh Guha, Patrick Huck, Hamming Howard Li, Matthew McDermott, et al · 2023
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The open dac 2023 dataset and challenges for sorbent discovery in direct air capture
Anuroop Sriram, Sihoon Choi, Xiaohan Yu, Logan M. Brabson, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, Andrew J. Medford, and David S. Sholl · 2024
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Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations
Yutack Park, Jaesun Kim, Seungwoo Hwang, and Seungwu Han · 2024
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Mattersim: A deep learning atomistic model across elements, temperatures and pressures
Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, et al · 2024
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orb-models github repository
Orbital Materials · 2024
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Improving machine-learning models in materials science through large datasets
Jonathan Schmidt, Tiago FT Cerqueira, Aldo H Romero, Antoine Loew, Fabian Jäger, Hai-Chen Wang, Silvana Botti, and Miguel AL Marques · 2024
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Generalizing denoising to non-equilibrium structures improves equivariant force fields
Yi-Lun Liao, Tess Smidt, and Abhishek Das · 2024
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The oc20 leaderboard
Meta Fundamental AI Research and collaborators · 2024
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Generalizing denoising to non-equilibrium structures improves equivariant force fields
Yi-Lun Liao, Tess Smidt, and Abhishek Das · 2024
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The omat24 dataset
Meta Fundamental AI Research · 2024
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The fair chemistry (fairchem) model repository
Meta Fundamental AI Research and Collaborators · 2024
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The omat24 trained model checkpoints
Meta Fundamental AI Research · 2024
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Materials project database versions
The Materials Project · 2024
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S. Raja, I. Amin, F. Pedregosa, and A. S. Krishnapriyan · 2024
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