Fetching the paper…
Reading the bibliography…
Generating novel crystalline materials has the potential to lead to advancements in fields such as electronics, energy storage, and catalysis.
Tables of oriented site symmetries in space groups
JDH Donnay and G Turrell · 1974
Earlier work this paper cites.
International tables for crystallography , volume 1
Theo Hahn, Uri Shmueli, and JC Wilson Arthur · 1983
Earlier work this paper cites.
Physical properties of crystals: their representation by tensors and matrices
John Frederick Nye · 1985
Earlier work this paper cites.
Generalized Gradient Approximation Made Simple
John P. Perdew, Kieron Burke, and Matthias Ernzerhof · 1996
Earlier work this paper cites.
Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2003
Earlier work this paper cites.
Abstract algebra , volume 3
David Steven Dummit and Richard M Foote · 2004
Earlier work this paper cites.
The inorganic crystal structure database (icsd)—present and future
Mariette Hellenbrandt · 2004
Earlier work this paper cites.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Johannes Gasteiger, Shankari Giri, Johannes T Margraf, and Stephan Günnemann · 2011
Earlier work this paper cites.
International Tables for Crystallography
Mois Ilia Aroyo · 2013
Earlier work this paper cites.
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
Earlier work this paper cites.
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis, June 2013
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
Earlier work this paper cites.
Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd)
James E Saal, Scott Kirklin, Muratahan Aykol, Bryce Meredig, and Christopher Wolverton · 2013
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward, Ankit Agrawal, Alok Choudhary, and Christopher Wolverton · 2016
Earlier work this paper cites.
Crystalgan: learning to discover crystallographic structures with generative adversarial networks
Asma Nouira, Nataliya Sokolovska, and Jean-Claude Crivello · 2018
Earlier work this paper cites.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
Earlier work this paper cites.
Smact: Semiconducting materials by analogy and chemical theory
Daniel W Davies, Keith T Butler, Adam J Jackson, Jonathan M Skelton, Kazuki Morita, and Aron Walsh · 2019
Earlier work this paper cites.
Inverse design of solid-state materials via a continuous representation
Juhwan Noh, Jaehoon Kim, Helge S Stein, Benjamin Sanchez-Lengeling, John M Gregoire, Alan Aspuru-Guzik, and Yousung Jung · 2019
Earlier work this paper cites.
Pyxtal: A python library for crystal structure generation and symmetry analysis
Scott Fredericks, Kevin Parrish, Dean Sayre, and Qiang Zhu · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Generative adversarial networks for crystal structure prediction
Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, and Yousung Jung · 2020
Earlier work this paper cites.
CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations
Thomas D. Kühne, Marcella Iannuzzi, Mauro Del Ben, Vladimir V. Rybkin, Patrick Seewald, Frederick Stein, Teodoro Laino, Rustam Z. Khaliullin, Ole Schütt, Florian Schiffmann, Dorothea Golze, Jan Wilhelm, Sergey Chulkov, Mohammad Hossein Bani-Hashemian, Valéry Weber, Urban Bor 𝐬 \mathbf{s} tnik, Mathieu Taillefumier, Alice Shoshana Jakobovits, Alfio Lazzaro, Hans Pabst, Tiziano Müller, Robert Schade, Manuel Guidon, Samuel Andermatt, Nico Holmberg, Gregory K. Schenter, Anna Hehn, Augustin Bussy, Fabian Belleflamme, Gloria Tabacchi, Andreas Glöß, Michael Lass, Iain Bethune, Christopher J. Mundy, Christian Plessl, Matt Watkins, Joost VandeVondele, Matthias Krack, and Jürg Hutter · 2020
Cited alongside, same era.
Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity
Nils ER Zimmermann and Anubhav Jain · 2020
Cited alongside, same era.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
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
Equivariance with learned canonicalization functions
Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio, and Siamak Ravanbakhsh · 2023
Later among the works it cites.
Matsciml: A broad, multi-task benchmark for solid-state materials modeling
Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings, Mikhail Galkin, and Santiago Miret · 2023
Later among the works it cites.
Towards symmetry-aware generation of periodic materials
Youzhi Luo, Chengkai Liu, and Shuiwang Ji · 2023
Later among the works it cites.
Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
Later among the works it cites.
The open matsci ML toolkit: A flexible framework for machine learning in materials science
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, and Matthew Spellings · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
E (n) equivariant normalizing flows
Victor Garcia Satorras, Emiel Hoogeboom, Fabian Fuchs, Ingmar Posner, and Max Welling · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Cited alongside, same era.
Rapid discovery of stable materials by coordinate-free coarse graining
Rhys E. A. Goodall, Abhijith S. Parackal, Felix A. Faber, Rickard Armiento, and Alpha A. Lee · 2022
Cited alongside, same era.
Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
Cited alongside, same era.
On the combinatorics of crystal structures: number of wyckoff sequences of given length
Wolfgang Hornfeck · 2022
Cited alongside, same era.
Equivariant networks for crystal structures
Oumar Kaba and Siamak Ravanbakhsh · 2022
Cited alongside, same era.
Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi S. Jaakkola · 2022
Cited alongside, same era.
Digress: Discrete denoising diffusion for graph generation
Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
Later among the works it cites.
Scalable diffusion for materials generation
Mengjiao Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, and Ekin Dogus Cubuk · 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.
Mattext: Do language models need more than text & scale for materials modeling?
Nawaf Alampara, Santiago Miret, and Kevin Maik Jablonka · 2024
Later among the works it cites.
matbench-genmetrics: A python library for benchmarking crystal structure generative models using time-based splits of materials project structures
Sterling G Baird, Hasan M Sayeed, Joseph Montoya, and Taylor D Sparks · 2024
Later among the works it cites.
Space group informed transformer for crystalline materials generation
Zhendong Cao, Xiaoshan Luo, Jian Lv, and Lei Wang · 2024
Later among the works it cites.
Artificial intelligence driving materials discovery? perspective on the article: Scaling deep learning for materials discovery
Anthony K Cheetham and Ram Seshadri · 2024
Later among the works it cites.
Jarvis-leaderboard: a large scale benchmark of materials design methods
Kamal Choudhary, Daniel Wines, Kangming Li, Kevin F Garrity, Vishu Gupta, Aldo H Romero, Jaron T Krogel, Kayahan Saritas, Addis Fuhr, Panchapakesan Ganesh, et al · 2024
Later among the works it cites.
Fine-tuned language models generate stable inorganic materials as text
Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C Lawrence Zitnick, and Zachary Ward Ulissi · 2024
Later among the works it cites.
Space group constrained crystal generation
Rui Jiao, Wenbing Huang, Yu Liu, Deli Zhao, and Yang Liu · 2024
Later among the works it cites.
Vector field oriented diffusion model for crystal material generation
Astrid Klipfel, Yaël Fregier, Adlane Sayede, and Zied Bouraoui · 2024
Later among the works it cites.
Flowmm: Generating materials with riemannian flow matching
Benjamin Kurt Miller, Ricky TQ Chen, Anuroop Sriram, and Brandon M Wood · 2024
Later among the works it cites.
Perspective on ai for accelerated materials design at the ai4mat-2023 workshop at neurips 2023
Santiago Miret, NM Anoop Krishnan, Benjamin Sanchez-Lengeling, Marta Skreta, Vineeth Venugopal, and Jennifer N Wei · 2024
Later among the works it cites.
Matbench discovery – a framework to evaluate machine learning crystal stability predictions, 2024
Janosh Riebesell, Rhys E. A. Goodall, Philipp Benner, Yuan Chiang, Bowen Deng, Alpha A. Lee, Anubhav Jain, and Kristin A. Persson · 2024
Later among the works it cites.
A space group symmetry informed network for o (3) equivariant crystal tensor prediction
Keqiang Yan, Alexandra Saxton, Xiaofeng Qian, Xiaoning Qian, and Shuiwang Ji · 2024
Later among the works it cites.
Wycryst: Wyckoff inorganic crystal generator framework
Ruiming Zhu, Wei Nong, Shuya Yamazaki, and Kedar Hippalgaonkar · 2024
Later among the works it cites.