Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models
Yin Kan Phua, Tsuyohiko Fujigaya, Koichiro Kato
Abstract
), 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.
Topics & Concepts
CommercializationDurabilityMembraneConductivityComputer scienceMaterials sciencePolymerProcess engineeringFuel cellsSet (abstract data type)Resource (disambiguation)Chemical engineeringMechanical engineeringChemistryEngineeringComposite materialLawComputer networkProgramming languagePolitical sciencePhysical chemistryBiochemistryFuel Cells and Related MaterialsMachine Learning in Materials ScienceMembrane-based Ion Separation Techniques