A deep learning protocol for analyzing and predicting ionic conductivity of anion exchange membranes

Theoretical Examination of the Hydroxide Transport in Cobaltocenium-Containing Polyelectrolytes

Polymers incorporating cobaltocenium groups have received attention as promising components of anion-exchange membranes (AEMs), exhibiting a good balance of chemical stability and high ionic conductivity. In this work, we analyze the hydroxide diffusion in the presence of cobaltocenium cations in an aqueous environment based on the molecular dynamics of model systems confined in one dimension to mimic the AEM channels. In order to describe the proton hopping mechanism, the forces are obtained from the electronic structure computed at the density-functional tight-binding level. We find that the hydroxide diffusion depends on the channel size, modulation of the electrostatic interactions by the solvation shell, and its rearrangement ability. Hydroxide diffusion proceeds via both the vehicular and structural diffusion mechanisms with the latter playing a larger role at low diffusion coefficients. The highest diffusion coefficient is observed under moderate water densities (around half the density of liquid water) when there are enough water molecules to form the solvation shell, reducing the electrostatic interaction between ions, yet there is enough space for the water rearrangements during the proton hopping. The effects of cobaltocenium separation, orientation, chemical modifications, and the role of nuclear quantum effects are also discussed.

Predicting the anion conductivities and alkaline stabilities of anion conducting membrane polymeric materials: development of explainable machine learning models

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.

Electrochemical Conversion of CO2 to CO Utilizing Quaternized Polybenzimidazole Anion Exchange Membrane

CO is a significant product of electrochemical CO₂ reduction (ECR) which can be mixed with H₂ to synthesize numerous hydrocarbons. Membranes, as separators, can significantly influence the performance of ECR. Herein, a series of quaternized polybenzimidazole (QAPBI) anion exchange membranes with different quaternization degrees are prepared for application in ECR. Among all QAPBI membranes, the QAPBI-2 membrane exhibits optimized physico-chemical properties. In addition, the QAPBI-2 membrane shows higher a Faraday efficiency and CO partial current density compared with commercial Nafion 117 and FAA-3-PK-130 membranes, at -1.5 V (vs. RHE) in an H-type cell. Additionally, the QAPBI-2 membrane also has a higher Faraday efficiency and CO partial current density compared with Nafion 117 and FAA-3-PK-130 membranes, at -3.0 V in a membrane electrode assembly reactor. It is worth noting that the QAPBI-2 membrane also has excellent ECR stability, over 320 h in an H-type cell. This work illustrates a promising pathway to obtaining cost-effective membranes through a molecular structure regulation strategy for ECR application.

Computational Approaches to Alkaline Anion-Exchange Membranes for Fuel Cell Applications

Anion-exchange membranes (AEMs) are key components in relatively novel technologies such as alkaline exchange-based membrane fuel cells and AEM-based water electrolyzers. The application of AEMs in these processes is made possible in an alkaline environment, where hydroxide ions (OH^-) play the role of charge carriers in the presence of an electrocatalyst and an AEM acts as an electrical insulator blocking the transport of electrons, thereby preventing circuit break. Thus, a good AEM would allow the selective transport of OH^- while preventing fuel (e.g., hydrogen, alcohol) crossover. These issues are the subjects of in-depth studies of AEMs-both experimental and theoretical studies-with particular emphasis on the ionic conductivity, ion exchange capacity, fuel crossover, durability, stability, and cell performance properties of AEMs. In this review article, the computational approaches used to investigate the properties of AEMs are discussed. The different modeling length scales are microscopic, mesoscopic, and macroscopic. The microscopic scale entails the ab initio and quantum mechanical modeling of alkaline AEMs. The mesoscopic scale entails using molecular dynamics simulations and other techniques to assess the alkaline electrolyte diffusion in AEMs, OH^- transport and chemical degradation in AEMs, ion exchange capacity of an AEM, as well as morphological microstructures. This review shows that computational approaches can be used to investigate different properties of AEMs and sheds light on how the different computational domains can be deployed to investigate AEM properties.

Materials discovery of ion-selective membranes using artificial intelligence

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.

Correlation between the Stability of Substituted Cobaltocenium and Molecular Descriptors

Metallocenium cations, used as a component in an anion exchange membrane of a fuel cell, demonstrate excellent thermal and alkaline stability, which can be improved by the chemical modification of the cyclopentadienyl rings with substituent groups. In this work, the relation between the bond dissociation energy (BDE) of the cobaltocenium (CoCp₂⁺) derivatives, used as a measure of the cation stability, and chemistry-informed descriptors obtained from the electronic structural calculations is established. The analysis of 12 molecular descriptors for 118 derivatives reveals a nonlinear dependence of the BDE on the electron donating-withdrawing character of the substituent groups coupled to the energy of the frontier molecular orbitals. A chemistry-informed feed-forward neural network trained using k-fold cross-validation over the modest data set is able to predict the BDE from the molecular descriptors with the mean absolute error of about 1 kcal/mol. The theoretical analysis suggests some promising modifications of cobaltocenium for experimental research. The results demonstrate that even for modest data sets the incorporation of the chemistry knowledge into the neural network architecture, e.g., through mindful selection and screening of the descriptors and their interactions, paves the way to gain new insight into molecular properties.