Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models
ABSTRACTAnion exchange membranes (AEMs) are core components in fuel cells and water electrolyzers, which are crucial to realize a sustainable hydrogen society. The low anion conductivity and durability of AEMs have hindered the commercialization of AEM-based devices, and research and development (R&...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Science and Technology of Advanced Materials |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/14686996.2023.2261833 |
| _version_ | 1827585924210360320 |
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| author | Yin Kan Phua Tsuyohiko Fujigaya Koichiro Kato |
| author_facet | Yin Kan Phua Tsuyohiko Fujigaya Koichiro Kato |
| author_sort | Yin Kan Phua |
| collection | DOAJ |
| description | ABSTRACTAnion exchange membranes (AEMs) are core components in fuel cells and water electrolyzers, which are crucial to realize a sustainable hydrogen society. The low anion conductivity and durability of AEMs have hindered the commercialization of AEM-based devices, and research and development (R&D) to improve AEM materials is often resource-intensive. Although machine learning (ML) is commonly used in many fields to accelerate R&D while reducing resource consumption, it is rarely used in the AEM field. Three problems hinder the adoption of ML models, namely, the low explainability of ML models; complication with expressing both homopolymers and copolymers in unity to train a single ML model; and difficulty in building a single ML model that comprehends various polymer types. This study presents the first ML models that solve all three problems. Our models predicted the anion conductivity for a diverse set of unseen AEM materials with high accuracy (root mean squared error = 0.014 S cm−1), regardless of their state (freshly synthesized or degraded). This enables virtual pre-synthesis screening of novel AEM materials, reducing resource consumption. Moreover, human-comprehensible prediction logic revealed new factors affecting the anion conductivity of AEM materials. Such capability to reveal new important variables for AEM materials design could shift the paradigm of AEM R&D. This proposed method is not limited to AEM materials, instead it presents a technology that is applicable to the diverse set of polymers currently available. |
| first_indexed | 2024-03-08T23:53:39Z |
| format | Article |
| id | doaj.art-55601b420af944aabccc8b122b698de4 |
| institution | Directory Open Access Journal |
| issn | 1468-6996 1878-5514 |
| language | English |
| last_indexed | 2024-03-08T23:53:39Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials |
| spelling | doaj.art-55601b420af944aabccc8b122b698de42023-12-13T09:35:32ZengTaylor & Francis GroupScience and Technology of Advanced Materials1468-69961878-55142023-12-0124110.1080/14686996.2023.2261833Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning modelsYin Kan Phua0Tsuyohiko Fujigaya1Koichiro Kato2Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, Fukuoka, JapanDepartment of Applied Chemistry, Graduate School of Engineering, Kyushu University, Fukuoka, JapanDepartment of Applied Chemistry, Graduate School of Engineering, Kyushu University, Fukuoka, JapanABSTRACTAnion exchange membranes (AEMs) are core components in fuel cells and water electrolyzers, which are crucial to realize a sustainable hydrogen society. The low anion conductivity and durability of AEMs have hindered the commercialization of AEM-based devices, and research and development (R&D) to improve AEM materials is often resource-intensive. Although machine learning (ML) is commonly used in many fields to accelerate R&D while reducing resource consumption, it is rarely used in the AEM field. Three problems hinder the adoption of ML models, namely, the low explainability of ML models; complication with expressing both homopolymers and copolymers in unity to train a single ML model; and difficulty in building a single ML model that comprehends various polymer types. This study presents the first ML models that solve all three problems. Our models predicted the anion conductivity for a diverse set of unseen AEM materials with high accuracy (root mean squared error = 0.014 S cm−1), regardless of their state (freshly synthesized or degraded). This enables virtual pre-synthesis screening of novel AEM materials, reducing resource consumption. Moreover, human-comprehensible prediction logic revealed new factors affecting the anion conductivity of AEM materials. Such capability to reveal new important variables for AEM materials design could shift the paradigm of AEM R&D. This proposed method is not limited to AEM materials, instead it presents a technology that is applicable to the diverse set of polymers currently available.https://www.tandfonline.com/doi/10.1080/14686996.2023.2261833Machine learning modelsexplainable AIanion exchange membranefunctional polymersfuel celldata-driven |
| spellingShingle | Yin Kan Phua Tsuyohiko Fujigaya Koichiro Kato Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models Science and Technology of Advanced Materials Machine learning models explainable AI anion exchange membrane functional polymers fuel cell data-driven |
| title | Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models |
| title_full | Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models |
| title_fullStr | Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models |
| title_full_unstemmed | Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models |
| title_short | Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models |
| title_sort | predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials development of explainable machine learning models |
| topic | Machine learning models explainable AI anion exchange membrane functional polymers fuel cell data-driven |
| url | https://www.tandfonline.com/doi/10.1080/14686996.2023.2261833 |
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