Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data
Abstract Effective designs of biodegradable polymers are highly desirable for achieving a sustainable society by decreasing environmental burden and replacing petroleum-based resources with biomass. Low-field NMR is one of the candidate techniques because it provides information on the higher-order...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00613-7 |
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| author | Ryo Fujita Yoshifumi Amamoto Jun Kikuchi |
| author_facet | Ryo Fujita Yoshifumi Amamoto Jun Kikuchi |
| author_sort | Ryo Fujita |
| collection | DOAJ |
| description | Abstract Effective designs of biodegradable polymers are highly desirable for achieving a sustainable society by decreasing environmental burden and replacing petroleum-based resources with biomass. Low-field NMR is one of the candidate techniques because it provides information on the higher-order structure and dynamics of polymers quickly and conveniently. Although machine learning approaches such as Bayesian optimization (BO) and convolutional neural networks (CNNs) are significant, there have been almost no reports on effective material design based on low-field nuclear magnetic resonance (NMR) data. This study proposes a method for optimizing polymer process conditions using CNN-based features extracted from relaxation curves. This approach identified important features related to material properties while reconstructing denoised relaxation curves of polylactic acid. BO of process conditions using these features achieved an optimization rate comparable to using material property values, suggesting that effective material design is possible without directly evaluating a large number of properties. This might be potentially insightful for the feasibility of a framework to accelerate polymer development through low-field NMR with minimal property data. |
| format | Article |
| id | doaj-art-133abe5832384df28fef6be09aa97a35 |
| institution | Kabale University |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-133abe5832384df28fef6be09aa97a352025-08-20T03:47:17ZengNature Portfolionpj Materials Degradation2397-21062025-06-01911910.1038/s41529-025-00613-7Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR dataRyo Fujita0Yoshifumi Amamoto1Jun Kikuchi2Graduate School of Medical Life Science, Yokohama City UniversityRIKEN Center for Sustainable Resource ScienceGraduate School of Medical Life Science, Yokohama City UniversityAbstract Effective designs of biodegradable polymers are highly desirable for achieving a sustainable society by decreasing environmental burden and replacing petroleum-based resources with biomass. Low-field NMR is one of the candidate techniques because it provides information on the higher-order structure and dynamics of polymers quickly and conveniently. Although machine learning approaches such as Bayesian optimization (BO) and convolutional neural networks (CNNs) are significant, there have been almost no reports on effective material design based on low-field nuclear magnetic resonance (NMR) data. This study proposes a method for optimizing polymer process conditions using CNN-based features extracted from relaxation curves. This approach identified important features related to material properties while reconstructing denoised relaxation curves of polylactic acid. BO of process conditions using these features achieved an optimization rate comparable to using material property values, suggesting that effective material design is possible without directly evaluating a large number of properties. This might be potentially insightful for the feasibility of a framework to accelerate polymer development through low-field NMR with minimal property data.https://doi.org/10.1038/s41529-025-00613-7 |
| spellingShingle | Ryo Fujita Yoshifumi Amamoto Jun Kikuchi Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data npj Materials Degradation |
| title | Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data |
| title_full | Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data |
| title_fullStr | Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data |
| title_full_unstemmed | Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data |
| title_short | Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data |
| title_sort | bayesian optimization of biodegradable polymers via machine learning driven features from low field nmr data |
| url | https://doi.org/10.1038/s41529-025-00613-7 |
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