Fetching the paper…
Reading the bibliography…
In recent years, progress has been made in generating new crystalline materials using generative machine learning models, though gaps remain in efficiently generating crystals based on target properties.
Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
Georg Kresse and Jürgen Furthmüller · 1996
Earlier work this paper cites.
Generalized gradient approximation made simple
John P Perdew, Kieron Burke, and Matthias Ernzerhof · 1996
Earlier work this paper cites.
New developments in the inorganic crystal structure database (icsd): accessibility in support of materials research and design
Alec Belsky, Mariette Hellenbrandt, Vicky Lynn Karen, and Peter Luksch · 2002
Earlier work this paper cites.
The inorganic crystal structure database (icsd)—present and future
Mariette Hellenbrandt · 2004
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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.
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.
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
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.
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Ankit Agrawal and Alok Choudhary · 2016
Cited alongside, same era.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
Cited alongside, same era.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Later among the works it cites.
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G Aberle, Shijing Sun, et al · 2022
Later among the works it cites.
Inverse design of 3d molecular structures with conditional generative neural networks
Niklas WA Gebauer, Michael Gastegger, Stefaan SP Hessmann, Klaus-Robert Müller, and Kristof T Schütt · 2022
Later among the works it cites.
Material symmetry recognition and property prediction accomplished by crystal capsule representation
Chao Liang, Yilimiranmu Rouzhahong, Caiyuan Ye, Chong Li, Biao Wang, and Huashan Li · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Machine learning force fields
Oliver T Unke, Stefan Chmiela, Huziel E Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T Schutt, Alexandre Tkatchenko, and Klaus-Robert Muller · 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.
Vaspkit: A user-friendly interface facilitating high-throughput computing and analysis using vasp code
Vei Wang, Nan Xu, Jin-Cheng Liu, Gang Tang, and Wen-Tong Geng · 2021
Cited alongside, same era.
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.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Later among the works it cites.
Improving image generation with better captions
James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al · 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.