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Diffusion models are the standard toolkit for generative modelling of 3D atomic systems.
Numerically stable algorithms for the computation of reduced unit cells
Ralf W Grosse-Kunstleve, Nicholas K Sauter, and Paul D Adams · 2004
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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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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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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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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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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
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Transformers are graph neural networks
Chaitanya Joshi · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Andrew S Rosen, Shaelyn M Iyer, Debmalya Ray, Zhenpeng Yao, Alan Aspuru-Guzik, Laura Gagliardi, Justin M Notestein, and Randall Q Snurr · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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Geom, energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi S. Jaakkola · 2022
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2022
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A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M Elena, Dávid P Kovács, Janosh Riebesell, Xavier R Advincula, Mark Asta, Matthew Avaylon, William J Baldwin, et al · 2023
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Improving image generation with better captions, 2023
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al · 2023
Cited alongside, same era.
Diffdock: Diffusion steps, twists, and turns for molecular docking
Gabriele Corso, Bowen Jing, Regina Barzilay, Tommi Jaakkola, et al · 2023
Cited alongside, same era.
Emu: Enhancing image generation models using photogenic needles in a haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma, Sam Tsai, Jialiang Wang, Rui Wang, Peizhao Zhang, Simon Vandenhende, Xiaofang Wang, Abhimanyu Dubey, et al · 2023
Cited alongside, same era.
Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling
Posebusters: Ai-based docking methods fail to generate physically valid poses or generalise to novel sequences
Martin Buttenschoen, Garrett M Morris, and Charlotte M Deane · 2024
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Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design
Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola · 2024
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An all-atom protein generative model
Alexander E Chu, Jinho Kim, Lucy Cheng, Gina El Nesr, Minkai Xu, Richard W Shuai, and Po-Ssu Huang · 2024
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Symphony: Symmetry-equivariant point-centered spherical harmonics for 3d molecule generation
Ameya Daigavane, Song Eun Kim, Mario Geiger, and Tess Smidt · 2024
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Scaling rectified flow transformers for high-resolution image synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al · 2024
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Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J Bartel, and Gerbrand Ceder · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Daniel Flam-Shepherd and Alán Aspuru-Guzik · 2023
Cited alongside, same era.
Benchmarking generated poses: How rational is structure-based drug design with generative models?
Charles Harris, Kieran Didi, Arian R Jamasb, Chaitanya K Joshi, Simon V Mathis, Pietro Lio, and Tom Blundell · 2023
Cited alongside, same era.
Illuminating protein space with a programmable generative model
John B Ingraham, Max Baranov, Zak Costello, Karl W Barber, Wujie Wang, Ahmed Ismail, Vincent Frappier, Dana M Lord, Christopher Ng-Thow-Hing, Erik R Van Vlack, et al · 2023
Cited alongside, same era.
MOFChecker v0.9.6, 2023
Kevin M Jablonka · 2023
Cited alongside, same era.
Crystal structure prediction by joint equivariant diffusion
Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, and Yang Liu · 2023
Cited alongside, same era.
On the expressive power of geometric graph neural networks
Chaitanya K Joshi, Cristian Bodnar, Simon V Mathis, Taco Cohen, and Pietro Lio · 2023
Cited alongside, same era.
MOFDiff: Coarse-grained diffusion for metal-organic framework design
Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi S. Jaakkola, and Jake A. Smith · 2024
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Diffusion meets flow matching: Two sides of the same coin
Ruiqi Gao, Emiel Hoogeboom, Jonathan Heek, Valentin De Bortoli, Kevin P. Murphy, and Tim Salimans · 2024
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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
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Navigating the design space of equivariant diffusion-based generative models for de novo 3d molecule generation
Tuan Le, Julian Cremer, Frank Noe, Djork-Arné Clevert, and Kristof T Schütt · 2024
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Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations
Yi-Lun Liao, Brandon M Wood, Abhishek Das, and Tess Smidt · 2024
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie · 2024
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Abdiffuser: full-atom generation of in-vitro functioning antibodies
Karolis Martinkus, Jan Ludwiczak, Wei-Ching Liang, Julien Lafrance-Vanasse, Isidro Hotzel, Arvind Rajpal, et al · 2024
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Flowmm: Generating materials with riemannian flow matching
Benjamin Kurt Miller, Ricky TQ Chen, Anuroop Sriram, and Brandon M Wood · 2024
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Structure-based drug design with equivariant diffusion models
Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, et al · 2024
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From molecules to materials: Pre-training large generalizable models for atomic property prediction
Nima Shoghi, Adeesh Kolluru, John R. Kitchin, Zachary Ward Ulissi, C. Lawrence Zitnick, and Brandon M Wood · 2024
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Flowllm: Flow matching for material generation with large language models as base distributions
Anuroop Sriram, Benjamin Kurt Miller, Ricky T. Q. Chen, and Brandon M. Wood · 2024
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Swallowing the bitter pill: Simplified scalable conformer generation
Yuyang Wang, Ahmed AA Elhag, Navdeep Jaitly, Joshua M Susskind, and Miguel Angel Bautista · 2024
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Scalable diffusion for materials generation
Sherry Yang, KwangHwan Cho, Amil Merchant, Pieter Abbeel, Dale Schuurmans, Igor Mordatch, and Ekin Dogus Cubuk · 2024
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An evaluation of unconditional 3d molecular generation methods
Martin Buttenschoen, Yael Ziv, Garrett M Morris, and Charlotte Deane · 2025
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Semlaflow–efficient 3d molecular generation with latent attention and equivariant flow matching
Ross Irwin, Alessandro Tibo, Jon Paul Janet, and Simon Olsson · 2025
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Mattergen: a generative model for inorganic materials design
Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, et al · 2025
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