SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines

Abstract Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the li...

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Main Authors: Shun Nanjo, Arifin, Hayato Maeda, Yoshihiro Hayashi, Kan Hatakeyama-Sato, Ryoji Himeno, Teruaki Hayakawa, Ryo Yoshida
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:npj Computational Materials
Online Access:https://doi.org/10.1038/s41524-024-01492-3
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author Shun Nanjo
Arifin
Hayato Maeda
Yoshihiro Hayashi
Kan Hatakeyama-Sato
Ryoji Himeno
Teruaki Hayakawa
Ryo Yoshida
author_facet Shun Nanjo
Arifin
Hayato Maeda
Yoshihiro Hayashi
Kan Hatakeyama-Sato
Ryoji Himeno
Teruaki Hayakawa
Ryo Yoshida
author_sort Shun Nanjo
collection DOAJ
description Abstract Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors. However, the enormous computational costs and technical challenges of automating computer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning. We developed SPACIER, an open-source software program that incorporates RadonPy, a Python library for fully automated polymer physical property calculations based on all-atom classical molecular dynamics, into a Bayesian optimization-based polymer design system to overcome these challenges. As a proof-of-concept study, we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number.
format Article
id doaj-art-efd069c7350f493b90d9217cc641f208
institution Kabale University
issn 2057-3960
language English
publishDate 2025-01-01
publisher Nature Portfolio
record_format Article
series npj Computational Materials
spelling doaj-art-efd069c7350f493b90d9217cc641f2082025-02-02T12:33:49ZengNature Portfolionpj Computational Materials2057-39602025-01-0111111110.1038/s41524-024-01492-3SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelinesShun Nanjo0Arifin1Hayato Maeda2Yoshihiro Hayashi3Kan Hatakeyama-Sato4Ryoji Himeno5Teruaki Hayakawa6Ryo Yoshida7The Graduate University for Advanced Studies, SOKENDAIRD Technology and Digital Transformation Center, JSR CorporationTokyo Institute of TechnologyThe Graduate University for Advanced Studies, SOKENDAITokyo Institute of TechnologyThe Graduate University for Advanced Studies, SOKENDAITokyo Institute of TechnologyThe Graduate University for Advanced Studies, SOKENDAIAbstract Machine learning has rapidly advanced the design and discovery of new materials with targeted applications in various systems. First-principles calculations and other computer experiments have been integrated into material design pipelines to address the lack of experimental data and the limitations of interpolative machine learning predictors. However, the enormous computational costs and technical challenges of automating computer experiments for polymeric materials have limited the availability of open-source automated polymer design systems that integrate molecular simulations and machine learning. We developed SPACIER, an open-source software program that incorporates RadonPy, a Python library for fully automated polymer physical property calculations based on all-atom classical molecular dynamics, into a Bayesian optimization-based polymer design system to overcome these challenges. As a proof-of-concept study, we synthesized optical polymers that surpass the Pareto boundary formed by the tradeoff between the refractive index and the Abbe number.https://doi.org/10.1038/s41524-024-01492-3
spellingShingle Shun Nanjo
Arifin
Hayato Maeda
Yoshihiro Hayashi
Kan Hatakeyama-Sato
Ryoji Himeno
Teruaki Hayakawa
Ryo Yoshida
SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines
npj Computational Materials
title SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines
title_full SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines
title_fullStr SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines
title_full_unstemmed SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines
title_short SPACIER: on-demand polymer design with fully automated all-atom classical molecular dynamics integrated into machine learning pipelines
title_sort spacier on demand polymer design with fully automated all atom classical molecular dynamics integrated into machine learning pipelines
url https://doi.org/10.1038/s41524-024-01492-3
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