Bias free multiobjective active learning for materials design and discovery

Bias free multiobjective active learning for materials design and discovery

Identifying optimal materials in multiobjective optimization problems represents a challenge for new materials design approaches. Here the authors develop an active-learning algorithm to optimize the Pareto-optimal solutions successfully applied to the in silico polymer design for a dispersant-based...

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Main Authors: Kevin Maik Jablonka, Giriprasad Melpatti Jothiappan, Shefang Wang, Berend Smit, Brian Yoo
Format: Article
Language:English
Published: Nature Publishing Group 2021-04-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-22437-0
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spelling doaj-5e35ebe9396e4369baa0deb7485d7deb2021-04-25T11:12:34ZengNature Publishing GroupNature Communications2041-17232021-04-0112111010.1038/s41467-021-22437-0Bias free multiobjective active learning for materials design and discoveryKevin Maik Jablonka0Giriprasad Melpatti Jothiappan1Shefang Wang2Berend Smit3Brian Yoo4Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL)BASF CorporationBASF CorporationLaboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL)BASF CorporationIdentifying optimal materials in multiobjective optimization problems represents a challenge for new materials design approaches. Here the authors develop an active-learning algorithm to optimize the Pareto-optimal solutions successfully applied to the in silico polymer design for a dispersant-based application.https://doi.org/10.1038/s41467-021-22437-0
collection DOAJ
language English
format Article
sources DOAJ
author Kevin Maik Jablonka
Giriprasad Melpatti Jothiappan
Shefang Wang
Berend Smit
Brian Yoo
spellingShingle Kevin Maik Jablonka
Giriprasad Melpatti Jothiappan
Shefang Wang
Berend Smit
Brian Yoo
Bias free multiobjective active learning for materials design and discovery
Nature Communications
author_facet Kevin Maik Jablonka
Giriprasad Melpatti Jothiappan
Shefang Wang
Berend Smit
Brian Yoo
author_sort Kevin Maik Jablonka
title Bias free multiobjective active learning for materials design and discovery
title_short Bias free multiobjective active learning for materials design and discovery
title_full Bias free multiobjective active learning for materials design and discovery
title_fullStr Bias free multiobjective active learning for materials design and discovery
title_full_unstemmed Bias free multiobjective active learning for materials design and discovery
title_sort bias free multiobjective active learning for materials design and discovery
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2021-04-01
description Identifying optimal materials in multiobjective optimization problems represents a challenge for new materials design approaches. Here the authors develop an active-learning algorithm to optimize the Pareto-optimal solutions successfully applied to the in silico polymer design for a dispersant-based application.
url https://doi.org/10.1038/s41467-021-22437-0
work_keys_str_mv AT kevinmaikjablonka biasfreemultiobjectiveactivelearningformaterialsdesignanddiscovery
AT giriprasadmelpattijothiappan biasfreemultiobjectiveactivelearningformaterialsdesignanddiscovery
AT shefangwang biasfreemultiobjectiveactivelearningformaterialsdesignanddiscovery
AT berendsmit biasfreemultiobjectiveactivelearningformaterialsdesignanddiscovery
AT brianyoo biasfreemultiobjectiveactivelearningformaterialsdesignanddiscovery
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