Yin Kan Phua

To accelerate the rational design of high-performance functional polymers, such as anion exchange membranes (AEMs), the establishment of chemically meaningful and actionable design guidelines is essential. Machine learning (ML) models, particularly artificial neural networks (ANNs), offer high predictive accuracy for such materials but suffer from limited interpretability due to their black-box nature. Although explainable artificial intelligence (XAI) methods such as SHapley Additive exPlanations (SHAP) provide a unified framework for model explanation, their application to ANN models is hindered by the expensive computation cost associated with the high dimensionality of molecular descriptors commonly used to represent polymer structures. In this study, a framework that combines statistical (minimum redundancy maximum relevance) and explainable ANN-based (permutation importance via ELI5) feature selection was developed, reducing the input space to 67 key descriptors. This dimensionality reduction enabled computationally feasible SHAP analysis while enhancing the predictive accuracy of the ANN by 40.87%. However, the resulting key descriptors were often difficult to interpret in physicochemical terms. To address this, large language models (LLMs) such as ChatGPT were employed to analyze descriptor source code and assist human experts in deriving chemically intuitive insights. By orchestrating XAI, LLM assistance, and expert knowledge, the framework successfully extracted design guidelines for AEMs. Based on these insights, two candidate AEM polymers with predicted anion conductivities ≥0.1 S cm⁻¹ at 80 °C were proposed, exceeding typical commercialization thresholds. This study illustrates a generalizable, explainable, and efficient pathway for integrating ML, XAI, and LLMs in polymer informatics, with broad applicability across descriptor-based materials research.

Although anion‐exchange membranes (AEMs) are commonly used in fuel cells and water electrolyzers, their widespread commercialization is hindered by problems such as low anion conductivity and durability. Moreover, the development of high‐performance AEMs remains complex and time consuming. Here, we address these challenges by proposing an innovative approach for the efficient design and screening of AEM polymers using unsupervised machine learning. Our model, which combines principal component analysis with uniform manifold approximation and projection, generates an intuitive map that clusters AEM polymers based on structural similarities without any predefined knowledge regarding anion conductivity or other experimentally derived variables. As a powerful navigation tool, this map provides insights into promising main‐chain structures, such as poly(arylene alkylene)s with consistently high conductivity and polyolefins with exceptional performance depending on the substituent. Furthermore, assisted by key molecular descriptors, inverse analysis with this model allows targeted design and property prediction before synthesis, which will significantly accelerate the discovery of novel AEM polymers. This work represents a paradigm shift not only in AEM research but also generally in materials research, moving from black‐box predictions toward interpretable guidelines that foster collaboration between researchers and machine learning for efficient and informed material development.

Recent shift towards clean energy increased the demand for both fuel cells (used as clean power generator) and water electrolyzer (used as hydrogen supply) significantly. Anion exchange membrane (AEM) serves as a core component for these devices, though its low anion conductivity and durability inhibit their potential for commercialization. Many research and development (R&D) have been done seeking for improvements in AEM¹, but current empirical-centric method consumes significant amount of resources, such as cost, labor, and time. To reduce resource consumptions, implementing materials informatics (MI) that allows high-speed screening of materials through a pre-trained AEM polymer machine learning (ML) model is important. However, AEMs are made up of polymers whose chemical structures are complex and hard to represent in a machine-understandable form. Fingerprints are often used to represent chemical structures in numerical forms generated through algorithm². Majority of these fingerprints are designed for small molecules, not polymers, but they are usually unintuitive and difficult to understand due to their topological nature. In contrast, nuclear magnetic resonance (NMR) chemical shift have long been used in chemistry to identify the chemical structure of a particular sample³. In this study, we aim to utilize the high-resolution nature of NMR chemical shift to identify structural formula as chemical structure fingerprint for ML model, such that a polymer-suited and highly explainable fingerprint can be developed. First, an AEM database containing structural and experimental condition information was built using data extracted from 62 papers. Experimental conditions included were anion conductivity measuring temperature, alkaline stability test measuring condition (test temperature, length of test, and concentration of alkaline solution). Then, the ¹³C NMR chemical shift for the chemical structure contained in structural information was calculated using ChemDraw. The obtained chemical shifts were converted to numerical strings and is named as “NMR fingerprint”. A new AEM database containing both structural (molar ratio of each building blocks and NMR fingerprints) and experimental condition was used as the training database for ML models. Target variable was set to anion conductivity, and the rest were explanatory variables. ML model used was XGBoost. Cross-validation was used to evaluate the capability of ML models to predict anion conductivity of novel AEM polymers. Prediction logic was analyzed using Shapley additive explanations (SHAP) value. The database built contains data from 62 AEM papers, with 2,197 anion conductivity data points. Each AEM chemical structures present in the database was converted to NMR fingerprints using NMR chemical shifts, obtaining around 2,000 NMR fingerprints for each AEM polymer unit. Together with the experimental conditions and structural information included in the database, the data were used as train-validation dataset for XGBoost. The coefficient of determination (R²) obtained for cross-validated model was 0.9235, implying that the model learnt and determined the relationship between anion conductivity and AEM polymer structure with high accuracy, with the aid of experimental conditions. Then, the prediction logic of the ML model was explored using SHAP values, which are values computed from coalitional game theory, and is used to increase transparency and interpretability of ML models. Analyzing the plot of SHAP values for top 20 important variables used in XGBoost showed that measuring temperature for anion conductivity ranked highest, which is in coherence to the well-known behavior of AEM polymers. Besides, non-experimental condition variables such as 29.8_A ranked into the top 3 important variables. 29.8_A is the chemical shift for alkyl groups attached in between two imidazolium group of AEM polymer, suggesting that the presence or absence of more than one imidazolium group per side chain is important to determine the anion conductivity of an AEM polymer. SHAP values for 29.8_A show that higher feature value (pink color) gives higher impact (positive region of x-axis) to the target variable, inferring that having alkyl groups between imidazolium groups give beneficial effect to anion conductivity. Such ability to explain the prediction logic of ML model shows that using NMR chemical shifts as fingerprints for AEM polymer structures provide intuitive, human-understandable ML prediction logic explanation. Together with the high cross-validation accuracy, NMR chemical shifts hold the potential to not only be a gold standard in expressing polymer structures in machine-understandable form, but also to strongly push the adoption of ML in the AEM polymer field, creating a paradigm shift for AEM R&D. Reference S. Gottesfeld et al., J. Power Sources 2018, 375, 170-184. J. Bajorath, J. Chem. Inf. Comput. Sci. 2001, 41, 2, 233–245. P. Jezzard et al., Adv. Mater. 1992, 4, 2, 82-90. Figure 1

Anion 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.

Polymer Electrolyte Membrane Fuel Cells (PEMFCs) play a centralized role in the decarbonization of energy systems due to their carbon-free electricity generation. However, low utilization efficiency of the Pt electrocatalyst has hindered the widespread adoption of PEMFCs. Herein, the effect of surface roughness on a catalyst layer (CL) based on carbon nanotubes (CNTs) in contact with polymer electrolyte membranes (PEMs) in PEMFCs was studied to examine the relationship between such surface roughness and Pt utilization efficiency. The surface roughness of the vacuum-filtered CL sheets was evaluated using a laser microscope. The surface of the CL sheet contacting the filter membrane was smoother than that of the surface exposed to air. When the CL sheet with the smoother surface was laminated with the PEM to fabricate a membrane electrode assembly (MEA), the power density of the single cell was 604.6 mW cm⁻² at 80 °C under 100% relative humidity (RH), which was greater than that of the MEA with a rougher CL surface (542.4 mW cm⁻²).