Infusing theory into deep learning for interpretable reactivity prediction

Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surface...

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Autores principales:	Shih-Han Wang, Hemanth Somarajan Pillai, Siwen Wang, Luke E. K. Achenie, Hongliang Xin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/d84456e3439c4cab9218e579eb71a020
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id	oai:doaj.org-article:d84456e3439c4cab9218e579eb71a020
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spelling	oai:doaj.org-article:d84456e3439c4cab9218e579eb71a0202021-12-02T17:19:40ZInfusing theory into deep learning for interpretable reactivity prediction10.1038/s41467-021-25639-82041-1723https://doaj.org/article/d84456e3439c4cab9218e579eb71a0202021-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25639-8https://doaj.org/toc/2041-1723Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces.Shih-Han WangHemanth Somarajan PillaiSiwen WangLuke E. K. AchenieHongliang XinNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Shih-Han Wang Hemanth Somarajan Pillai Siwen Wang Luke E. K. Achenie Hongliang Xin Infusing theory into deep learning for interpretable reactivity prediction
description	Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces.
format	article
author	Shih-Han Wang Hemanth Somarajan Pillai Siwen Wang Luke E. K. Achenie Hongliang Xin
author_facet	Shih-Han Wang Hemanth Somarajan Pillai Siwen Wang Luke E. K. Achenie Hongliang Xin
author_sort	Shih-Han Wang
title	Infusing theory into deep learning for interpretable reactivity prediction
title_short	Infusing theory into deep learning for interpretable reactivity prediction
title_full	Infusing theory into deep learning for interpretable reactivity prediction
title_fullStr	Infusing theory into deep learning for interpretable reactivity prediction
title_full_unstemmed	Infusing theory into deep learning for interpretable reactivity prediction
title_sort	infusing theory into deep learning for interpretable reactivity prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d84456e3439c4cab9218e579eb71a020
work_keys_str_mv	AT shihhanwang infusingtheoryintodeeplearningforinterpretablereactivityprediction AT hemanthsomarajanpillai infusingtheoryintodeeplearningforinterpretablereactivityprediction AT siwenwang infusingtheoryintodeeplearningforinterpretablereactivityprediction AT lukeekachenie infusingtheoryintodeeplearningforinterpretablereactivityprediction AT hongliangxin infusingtheoryintodeeplearningforinterpretablereactivityprediction
_version_	1718381052351741952

Infusing theory into deep learning for interpretable reactivity prediction

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