Date/Time
Date(s) - 11/19/2024
3:00 pm - 4:00 pm
Location
Rhines 125
Categories
Abstract
As we push the boundaries of materials for applications in ever-increasing extreme environments, novel and often complex materials are needed that require creative design strategies from electron-to-microstructure levels. To understand the intertwined electronic and atomic mechanisms in complex materials, the traditional computational tools that have been highly successful now need to be integrated with sophisticated methods. A fitting example is high entropy materials (HEMs) that consist of multiple principal elements in large proportions in contrast to one principal element in conventional/dilute alloys. Robust data-science methods offer a rigorous path forward to overcome the multi-dimensional challenge.
Our group uses machine learning algorithms in conjunction with physics-based principles and databases to unveil key structure-property correlations that are otherwise unintuitive in complex materials. In this presentation, I will discuss our new data-science integrated computational materials science approach, namely PREDICT (Predict properties from Existing Databases in Complex materials Territory), whereby properties in complex alloys are predicted by learning from simpler alloys. I will also discuss how charge-density can be used as a universal descriptor for properties’ prediction. I will also discuss database frameworks being developed in our group.
Bio
Dilpuneet Aidhy, Ph.D.
Associate Professor
Clemson University
Dr. Dilpuneet Aidhy is an Associate Professor in the Department of Materials Science and Engineering at Clemson University. His expertise is in computational materials science, including density functional theory, molecular dynamics simulations, and machine learning applied to solid-state materials. His areas of interest include metallic alloys and ceramic oxides. His work is primarily focused on understanding the thermodynamics and kinetics of defects, grain boundaries, mechanical and radiation damage properties, ion transport, and electrochemistry in functional oxides. In the past few years, his work has extensively focused on developing data science-based methods to predict properties of high entropy materials.
He is on the editorial board of Computational Materials Science, Scientific Reports, and Frontiers in Materials. He received his Ph.D. in materials science and engineering from the University of Florida in 2009.