Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models
Autor: | Yin Kan Phua, Tsuyohiko Fujigaya, Koichiro Kato |
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Jazyk: | angličtina |
Rok vydání: | 2023 |
Předmět: | |
Zdroj: | Science and Technology of Advanced Materials, Vol 24, Iss 1 (2023) |
Druh dokumentu: | article |
ISSN: | 14686996 1878-5514 1468-6996 |
DOI: | 10.1080/14686996.2023.2261833 |
Popis: | 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. |
Databáze: | Directory of Open Access Journals |
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