Machine Learning Algorithm Prediction and Synthesis of Next Generation Superhard Functional Materials
The goal of this project is to discover new materials that possess the properties needed to enable the technologies of the future. Many materials have outstanding properties that make them desirable for some applications but are deficient in other properties that limit their use. A well-known example is diamond, which is the hardest known material but is also an electrical insulator. Is there a material yet to be discovered that could satisfy the need for a superhard material that also has the high electrical conductivity of a metal or other useful properties?
This project will combine diverse areas of expertise to search for new superhard materials that also possess other desirable properties that enable them to fulfill uniquely demanding technological requirements. Both three-dimensional and two-dimensional forms of these materials will be synthesized. A feedback loop between experiment and theory will be used to characterize the materials, rationally design those with desired properties, and optimize the synthesis protocols. Students will be trained in an interdisciplinary collaborative team of theoreticians and experimentalists whose expertise includes chemistry, physics, and materials science and engineering.
This project will combine diverse areas of expertise to search for new superhard materials that also possess other desirable properties that enable them to fulfill uniquely demanding technological requirements. Both three-dimensional and two-dimensional forms of these materials will be synthesized. A feedback loop between experiment and theory will be used to characterize the materials, rationally design those with desired properties, and optimize the synthesis protocols. Students will be trained in an interdisciplinary collaborative team of theoreticians and experimentalists whose expertise includes chemistry, physics, and materials science and engineering.
Publications
Research Highlights
Impact of Data Bias on Machine Learning for Crystal Compound Synthesizability Predictions
Sara Kadkhodaei (U. Illinois - Chicago) Eva Zurek (SUNY – Buffalo)
2/4/2025
Heating Samples to 2000° C for Scanning Tunneling Microscopy Studies in Ultrahigh Vacuum
Michael Trenary (U. Illinois - Chicago)
2/4/2025
XtalOpt: Multi-objective Evolutionary Search for Novel Functional Materials
Eva Zurek (SUNY-Buffalo)
2/4/2025
A New Pathway to Resilient Materials
Sara Kadkhodaei and Russell Hemley (University of Illinois-Chicago)
6/20/2025
Ultrahard WB2 Superconducts under Pressure
R. Hemley (U. IL-Chicago)R. Hennig, J. Hamlin, P. Hirshfeld, G. Stewart (U. FL)
5/25/2023
Discovery of Giant “Wine-Rack” Negative Linear Compressibility in Copper Cyanide
Russell Hemley (University of Illinois-Chicago)
6/20/2025
Voxel Image Representation Learning of Crystalline Materials for Formation Energy Prediction
Sara Kadkhodaei (University of Illinois-Chicago)
6/20/2025
Prediction and Synthesis of Next Generation Superhard Functional Materials
Russell Hemley-(University of Illinois Chicago)
6/20/2025
Evolutionary Algorithm for the Discovery and Design of Metastable Phases
Eva Zurek (SUNY-Buffalo)
6/20/2025
Prediction and Synthesis of Next Generation Superhard Functional Materials
Eva Zurek (SUNY-Buffalo)
6/20/2025
Superconducting Material Stabilized at Ambient Pressure: A Step Toward Real-world Applications
Russell Hemley (University of Illinois-Chicago) Eva Zurek (SUNY-Buffalo)
6/25/2025
Educational and Outreach Activities in Kadkhodaei’s Lab
Sara Kadkhodaei (University of Illinois-Chicago)
6/20/2025
View All Highlights