Study of Deep Generative Models for Inorganic Chemical Compositions

Study of Deep Generative Models for Inorganic Chemical Compositions

25 October 2019

Yoshihide Sawada

Papers citing "Study of Deep Generative Models for Inorganic Chemical Compositions"

8 / 8 papers shown

Title
Vector Field Oriented Diffusion Model for Crystal Material Generation Astrid Klipfel Yael Fregier A. Sayede Zied Bouraoui DiffM 19 4 0 20 Dec 2023
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering Lipichanda Goswami Manoj Deka Mohendra Roy AI4CE 37 19 0 15 Sep 2022
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints Yong Zhao Edirisuriya M Dilanga Siriwardane Zhenyao Wu Nihang Fu Mohammed Al-Fahdi Ming Hu Jianjun Hu AI4CE 36 68 0 27 Mar 2022
Crystal Diffusion Variational Autoencoder for Periodic Material Generation Tian Xie Xiang Fu O. Ganea Regina Barzilay Tommi Jaakkola DiffM BDL 212 232 0 12 Oct 2021
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art Jianjun Hu Stanislav Stefanov Yuqi Song Sadman Sadeed Omee Steph-Yves M. Louis Edirisuriya M Dilanga Siriwardane Yong Zhao 11 33 0 09 Sep 2021
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties Zekun Ren S. Tian Juhwan Noh Felipe Oviedo G. Xing ... Qianxiao Li Senthilnath Jayavelu K. Hippalgaonkar Yousung Jung Tonio Buonassisi AI4CE 35 130 0 15 May 2020
Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials Yabo Dan Yong Zhao Xiang Li Shaobo Li Ming Hu Jianjun Hu AI4CE GAN 27 192 0 12 Nov 2019
Conditional Image Synthesis With Auxiliary Classifier GANs Augustus Odena C. Olah Jonathon Shlens GAN 244 3,190 0 30 Oct 2016