Exploring Functional Materials for Energy Applications: A Multiscale Modeling and Machine Learning Approach

Omar Allam, Georgia Institute of Technology
CPS Seminar Graphic

This presentation outlines my graduate research, leveraging multiscale modeling and machine learning to investigate functional materials for a range of energy storage and conversion applications. In the realm of alkali-ion batteries, my focus has been on designing organics for cathodic applications with enhanced electrochemical activity and on analyzing the nanophase morphology and ion transport in novel solid polymer electrolytes with robust mechanical properties. My energy conversion research encompasses investigating spectral instabilities and degradation mechanisms in 2D perovskites, as well as uncovering mechanisms for finetuning their band gaps. Further, I worked on optimizing organic conducting polymers and crystalline phase change materials for use as military obscurants. Most recently, at Google X, I developed a thermodynamically guided DFT workflow, augmented with machine learning interatomic potentials, to streamline materials discovery for a confidential project. These investigations employed a comprehensive array of modeling and machine learning methods for materials discovery and characterization.

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