Inverse design with deep generative models: next step in materials discovery

Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery.

Detalles Bibliográficos
Autores principales: Lu, Shuaihua, Zhou, Qionghua, Chen, Xinyu, Song, Zhilong, Wang, Jinlan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Perspective
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385454/
https://www.ncbi.nlm.nih.gov/pubmed/35992238
http://dx.doi.org/10.1093/nsr/nwac111
_version_ 1784769597603315712
author Lu, Shuaihua
Zhou, Qionghua
Chen, Xinyu
Song, Zhilong
Wang, Jinlan
author_facet Lu, Shuaihua
Zhou, Qionghua
Chen, Xinyu
Song, Zhilong
Wang, Jinlan
author_sort Lu, Shuaihua
collection PubMed
description Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery.
format Online
Article
Text
id pubmed-9385454
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-93854542022-08-18 Inverse design with deep generative models: next step in materials discovery Lu, Shuaihua Zhou, Qionghua Chen, Xinyu Song, Zhilong Wang, Jinlan Natl Sci Rev Perspective Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery. Oxford University Press 2022-06-11 /pmc/articles/PMC9385454/ /pubmed/35992238 http://dx.doi.org/10.1093/nsr/nwac111 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Perspective
Lu, Shuaihua
Zhou, Qionghua
Chen, Xinyu
Song, Zhilong
Wang, Jinlan
Inverse design with deep generative models: next step in materials discovery
title Inverse design with deep generative models: next step in materials discovery
title_full Inverse design with deep generative models: next step in materials discovery
title_fullStr Inverse design with deep generative models: next step in materials discovery
title_full_unstemmed Inverse design with deep generative models: next step in materials discovery
title_short Inverse design with deep generative models: next step in materials discovery
title_sort inverse design with deep generative models: next step in materials discovery
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9385454/
https://www.ncbi.nlm.nih.gov/pubmed/35992238
http://dx.doi.org/10.1093/nsr/nwac111
work_keys_str_mv AT lushuaihua inversedesignwithdeepgenerativemodelsnextstepinmaterialsdiscovery
AT zhouqionghua inversedesignwithdeepgenerativemodelsnextstepinmaterialsdiscovery
AT chenxinyu inversedesignwithdeepgenerativemodelsnextstepinmaterialsdiscovery
AT songzhilong inversedesignwithdeepgenerativemodelsnextstepinmaterialsdiscovery
AT wangjinlan inversedesignwithdeepgenerativemodelsnextstepinmaterialsdiscovery

Ejemplares similares