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Wei Chen, assistant professor of materials science and engineering in Armour College of Engineering’s Department of Mechanical, Materials, and Aerospace Engineering, has received the National Science Foundation’s Faculty Early Career Development Program (CAREER) award for innovative machine learning studies of high-entropy alloys. This prestigious award acknowledges Chen’s contributions to computational discovery and the design of new materials with high-performance computing and data mining titled “.”

The CAREER award supports junior faculty who exemplify the role of teacher-scholar through outstanding research and the integration of education within their institutions. Chen’s recognition promotes educational activities to develop quantum mechanical and machine learning methods to understand and design complex, multi-element alloys at the atomic level.

“The multidisciplinary nature of this project brings perspectives from multiple academic fields into the forefront of materials research. The focus of the technologically relevant, complex-concentrated alloys will strengthen U.S. leadership in fundamental alloy research,” says Chen.

As the principal investigator, Chen will collaborate with his graduate students to design new high-entropy alloys using machine learning to find the compositions and structures that will result in optimal mechanical and functional properties. The purpose of this research is to design enhanced alloys that can be used to improve mechanical applications across national defense, consumer electronics, and the aerospace and automotive industries.

Different from conventional alloys that have a single principal element, such as aluminum or titanium, these multi-principal component alloys with five or more elements are challenging to design. “High-entropy alloys are also difficult to manufacture and require both expensive materials and specialty processing techniques,” says Chen.

These studies will help researchers develop materials that are both ductile and strong, while producing composition designs that reduce costs. This award gives students the opportunity to be among the early researchers to conduct these advanced, machine learning, and computational investigations. With access to supercomputers, which have the ability to perform at or near the currently highest operational rate for computers, students will examine how atoms are arranged in alloys and identify new, efficient ways to design complex materials systems.

Chen plans to work closely with undergraduate and graduate students to foster multidisciplinary career development through such project-based research programs. “By using state-of-the-art artificial intelligence to advance the design of materials, we are promoting Illinois Tech’s strategic goal of growing our computational and computing capacities in research and education through data analytics and machine learning,” says Chen.

This award will also provide the opportunity to introduce younger students to the basic principles of coding and machine learning. He adds, “We plan on collaborating with SMASH Illinois to offer academic and social programs to underrepresented students and broaden participation in materials education.”

The CAREER award is effective as of May 1, 2020, and estimated to continue until April 30, 2025. This project is jointly supported by the National Science Foundation’s Division of Materials Research and the Office of Advanced Cyberinfrastructure.

Wei Chen, “First-Principles Predictive Understanding of Chemical Order in Complex Concentrated Alloys: Structures, Dynamics, and Defect Characteristics,” National Science Foundation ($500,000); NSF Grant Number: 1954380

Photo: Assistant Professor of Materials Science and Engineering Wei Chen