Fetching the paper…
Reading the bibliography…
Recent advances in deep learning have enabled the generation of realistic data by training generative models on large datasets of text, images, and audio.
New cubic perovskites for one-and two-photon water splitting using the computational materials repository
Ivano E Castelli, David D Landis, Kristian S Thygesen, Søren Dahl, Ib Chorkendorff, Thomas F Jaramillo, and Karsten W Jacobsen · 2012
Earlier work this paper cites.
Computational screening of perovskite metal oxides for optimal solar light capture
Ivano E Castelli, Thomas Olsen, Soumendu Datta, David D Landis, Søren Dahl, Kristian S Thygesen, and Karsten W Jacobsen · 2012
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.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
Earlier work this paper cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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.
Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 2018
Earlier work this paper cites.
Data-driven approach to encoding and decoding 3-d crystal structures
Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, and Yoshua Bengio · 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.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2019
Earlier work this paper cites.
A critical review of machine learning of energy materials
Chi Chen, Xunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, and Shyue Ping Ong · 2020
Earlier work this paper cites.
Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, and William Chan · 2020
Earlier work this paper cites.
3-d inorganic crystal structure generation and property prediction via representation learning
Callum J. Court, Batuhan Yildirim, Apoorv Jain, and Jacqueline M. Cole · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Machine-enabled inverse design of inorganic solid materials: promises and challenges
Juhwan Noh, Geun Ho Gu, Sungwon Kim, and Yousung Jung · 2020
Cited alongside, same era.
Airss data for carbon at 10gpa and the c+n+h+o system at 1gpa
Chris J. Pickard · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
Cited alongside, same era.
Machine learning for catalysis informatics: Recent applications and prospects
Takashi Toyao, Zen Maeno, Satoru Takakusagi, Takashi Kamachi, Ichigaku Takigawa, and Ken ichi Shimizu · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Crystal-gfn: sampling crystals with desirable properties and constraints
Mila AI4Science, Alex Hernandez-Garcia, Alexandre Duval, Alexandra Volokhova, Yoshua Bengio, Divya Sharma, Pierre Luc Carrier, Yasmine Benabed, Michał Koziarski, and Victor Schmidt · 2023
Later among the works it cites.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2023
Later among the works it cites.
Masked diffusion transformer is a strong image synthesizer
Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan · 2023
Later among the works it cites.
3d equivariant diffusion for target-aware molecule generation and affinity prediction
Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma · 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.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Cited alongside, same era.
Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Cited alongside, same era.
Crystal diffusion variational autoencoder for periodic material generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola · 2021
Cited alongside, same era.
Do transformers really perform bad for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
Cited alongside, same era.
High-throughput discovery of novel cubic crystal materials using deep generative neural networks
Yong Zhao, Mohammed Al-Fahdi, Ming Hu, dirisuriya M. D. Siriwardane, Yuqi Song, Alireza Nasiri, and Jianjun Hu · 2021
Cited alongside, same era.
Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling · 2022
Cited alongside, same era.
Ali Hatamizadeh, Jiaming Song, Guilin Liu, Jan Kautz, and Arash Vahdat · 2023
Later among the works it cites.
Crystal structure prediction by joint equivariant diffusion
Rui Jiao, Wenbing Huang, Peijia Lin, Jiaqi Han, Pin Chen, Yutong Lu, and Yang Liu · 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.
OpenAI · 2023
Later among the works it cites.
Moldiff: Addressing the atom-bond inconsistency problem in 3d molecule diffusion generation
Xingang Peng, Jiaqi Guan, Qiang Liu, and Jianzhu Ma · 2023
Later among the works it cites.
Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
Later among the works it cites.
De novo design of protein structure and function with rfdiffusion
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles Basile I. M., Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker · 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, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Ryota Tomioka, and Tian Xie · 2023
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
Closest in time.
Complete and efficient graph transformers for crystal material property prediction
Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, and Shuiwang Ji · 2024
Closest in time.