Molecular Simulations and Machine Learning for Computational Design of Materials with Fast Oxygen Kinetics

media release: 3:30-4:30pm, Room 1115 Wisconsin Energy Institute and online. This event is offered both in-person at the Wisconsin Energy Institute and online through Zoom Webinar. If attending online, registration is required. Click here to register for this and all other webinars as part of the Sustainable Energy Seminar series in Fall 2024.

Join us on September 23 at 3:30 p.m. for this Sustainable Energy Seminar presentation by Dr. Dane Morgan, professor of materials science and engineering at UW-Madison.

Abstract

Rapid transport of oxygen across and through materials is at the heart of technologies from solid oxide fuel cells to gas separation membranes to memristors. Almost 200 years of experimental study and materials theory have given us a deep understanding of the mechanisms by which ions, and in particular oxygen, move through solid state systems, as well as significant quantities of data on relevant kinetic processes. Until recently researchers have had to rely on this understanding, qualitative intuitions, and extensive trial and error to find new oxygen conducting materials. However, in recent years developments in computational approaches, including accurate high-throughput molecular simulations and data-centric descriptor and machine learning methods, have emerged as powerful tools in the search for the next generation of fast oxygen materials. In this talk I will discuss work from my group on computational discovery of new oxygen conducting materials. I will first describe the development of BaFe_0.125Co_0.125Zr_0.75O₃, a new cathode material for solid oxide fuel cells. Then I will describe the more fundamental search for new interstitial oxygen conductors, describing how we approached discovering these somewhat rare materials and the specific identification of the first computationally discovered interstitial oxygen conductor material, La₄Mn₅Si₄O_22+d.

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we could stimulate this process.