Probabilistic Machine Learning Driven Discovery and Design of New Materials for Sustainable Energy and Transport

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Providing clean, reliable and environment-friendly energy is a critical global challenge. To overcome this, the transportation and energy industries are undergoing a paradigm shift by adopting newer and better materials technologies. For speeding up the process of materials development, traditional trial-and-error based experimental approaches are being replaced by a synergistic integration of computational materials science with targeted experimentation. My group focuses on using this Integrated Computational Materials Engineering approach: (i) to design lighter, stronger, and tougher materials for automotive and aerospace structures to boost fuel economy while maintaining their safety and performance; and (ii) to discover novel materials to make sustainable energy technologies such as batteries, catalysts, and solar cells more efficient and cost-effective. The first theme caters towards improving energy efficiency while the latter towards developing new technologies for clean energy production. Designing new materials is, however, quite complex and in this respect, the emerging field of machine learning (ML) can help accelerate the pace of materials development by capturing patterns from data consisting of a multitude of variables that are difficult to capture from human intuition. The overarching goal of the proposed research program is to design and discover new materials for lightweight transportation and sustainable energy by effectively combining mathematically robust Bayesian machine learning techniques with physically accurate atomistic modeling. For structural materials, the aim is to develop multiscale material models with high fidelity and efficiency that are able to predict the global response including failure. Using ML on datasets generated by high-throughput density functional theory computations, the proposed research will also: (i) map out the structure-mechanical property relationships for a wide range of two dimensional materials, (ii) screen electrode materials for metal-air batteries with optimum capacity and life-time performance, (iii) design gas-phase catalysts for CO2 reduction, and (iv) develop robust interatomic potentials for steels widely used in structural applications. Our long-term vision is to physically realize proposed material designs and commercialize them in close collaboration with experimental and industry partners. The proposed program will contribute by developing new scientific knowledge and materials technologies for NSERC's target areas in Advanced Manufacturing and train six PhD students as future leaders in the energy, manufacturing and transportation industries. Practically, it will lead to design tools for the Canadian manufacturing industry to create stronger and tougher lightweight materials, new battery materials for automotives, and new catalysts for solar energy conversion.