Computational Modeling and Machine Learning Approaches for Accelerated Materials Design

A substantial part of research in energy storage and conversion systems concerns the discovery and characterization of novel materials with improved performance, but most advancements are still generally attributed to costly and time-consuming trial-and-error experimentation. Computational chemistry methods combined with machine learning techniques offer paradigm shift in how materials are fundamentally understood and designed. Namely, high-throughput calculations combined with machine learning can help evaluate different properties of complex materials and efficiently screen millions of candidates to identify the ones with improved targeted properties, such as higher activity and selectivity and/or enhanced durability. In this talk we will first illustrate the use of density functional theory (DFT) in the design of fuel cell and electrolyzer ionomers. More specifically, we will discuss how simple descriptors such as DFT-calculated adsorption energies and adsorption modes of polymer fragments can be used to guide the rational design of ionomers for anion exchange membrane fuel cells and alkaline membrane water electrolyzers. Our design principle has led to the synthesis of new polymers, e.g., poly(fluorene) and polynorbornene-based ionomers, with weak adsorption on the electrocatalyst surface and improved hydrogen oxidation reaction activity. We will further introduce the computational workflow that integrates high-throughput DFT calculations with machine learning with a goal of designing new amine-based CO 2 adsorbents, as well as platinum group metal-free electrocatalysts for conversion of CO 2 to value added products. In our workflow we use descriptors together with DFT-calculated binding energetics to train artificial neural networks for activity prediction. Our trained machine learning models are then used to screen millions of candidate chemistries and identify the ones with optimal activity. Descriptor importance evaluation provides additional design principles based on the structure to property relationships for the synthesis of targeted materials for CO 2 capture and electro-conversion.