Machine ​Learning for Modeling Complex Materials

Speaker: Serveh Kamravah, University of Wyoming

Abstract
In recent years, machine learning (ML) approaches have made it possible to extract and explore intricate patterns from big data. One of the fields that can benefit from the computational advantages that ML offers is materials characterization where we have complex heterogeneous morphology. The morphology of complex systems is one of the determinant elements that control a variety of their properties, such as flow, transport, mechanical and thermal behaviors. Such properties are often estimated using experimental and computational methods, which can be very costly and time-demanding. As such, faster and more automatic methods are required. Machine learning provides an alternative solution to this problem. In my presentation, I will talk about a deep learning model (DL) that can take the 3D morphology of complex materials and estimate their transport properties. Then, I will talk about applying DL for quantifying the accuracy of augmentation methods (used for constructing the large dataset) and identifying the method that can provide the best set of data by minimizing the discrepancy and expanding the variability. I will also discuss the application of deep learning for dynamic data when they change with time for a transport problem on a complex membrane system. I close this particular topic by describing how the governing equations can be used in DL for filling the gap in data and reducing the amount of training data.

Bio
Dr. Serveh Kamrava joined the Department of Chemical Engineering at the University of Wyoming as an Assistant Professor in August 2021. She earned her Ph.D. in Chemical Engineering from the University of Southern California in 2020 where her research was focused on Complex Porous Materials Modeling using Machine Learning. She is a member of the Society of Women Engineers (SWE), the American Institute of Chemical Engineers (AIChE), and the American Physical Society (APS). Her research has been published in journals such as Neural Networks, Journal of Membrane Science, Physical Review E, Advances in Water Resources, Transport in Porous Media, and Nature Computational Materials.