MaterIQ: Accelerating Material Discovery using Artificial Intelligence

Abstract

MaterIQ, or “Material IQ,” is a research project co-lead by Profs. Stephen Baek (UVA Data Science) and H.S. Udaykumar (Iowa Engineering). The project aims to develop a suite of artificial intelligence (AI) and machine learning (ML) algorithms specifically tailored for materials science research. We envision that AI/ML algorithms can be used in various stages of material discovery cycle such as characterization, property prediction, synthesis, design optimization, inverse design, etc. To this end, we combine diverse multidisciplinary expertise across mechanical engineering, data science, computer science, physics, chemistry, statistics, and others, to understand and formulate unique problems arising in materials research using the languages of AI/ML. The project is well funded by multiple funding sources, including the National Science Foundation’s Designing Materials to Revolutionize and Engineer our Future (DMREF) Program (DMR #2203580; PI Baek), Air Force Office of Scientific Research (AFOSR) Multidisciplinary University Research Initiatives (MURI) Program (FA9550-19-1-0318; PI Sewell & Udaykumar), Army Research Office Seedling Grant Program (Pending; PI Udaykumar), etc.

Keywords: Physics-aware machine learning, Scientific machine learning (SciML), Material genome initiative, Materials-by-design

 

Awards & Recognitions

List under construction

1. Defense Innovation Award
2. Best Poster Award

 

Journal Publications

Microstructure Design & Synthesis

1. Chun, S., Roy, S., Nguyen, Y.-T., Choi, J.B., Udaykumar, H.S., & Baek, S. (2020). Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials, Scientific Reports, 10:13307.
   https://www.nature.com/articles/s41598-020-70149-0
   Source Code: Coming Soon!

2. Nguyen, P.C.H., Vlassis, N.N., Bahmani, B., Sun, W.-C., Udaykumar, H.S., & Baek, S. (2022). Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning, Scientific Reports, 12:9034.
   https://www.nature.com/articles/s41598-022-12845-7
   Source Code: Coming Soon!

3. List under construction

Physics Aware Machine Learning

List under construction

 

Conference Presentations

List under construction

 

Other Contributions

List under construction

 

AI in Materials Science Webinar/Workshop Series

Coming soon!

 

Patents

Under preparation

 

The Team

Principal Investigators

• Stephen Baek, Ph.D. (University of Virginia)
• H.S. Udaykumar, Ph.D. (University of Iowa)

Other Senior Personnel

• Phong Nguyen, Ph.D. (University of Virginia)
• Xuan Song, Ph.D. (University of Iowa)
• Jia-Wei Chern, Ph.D. (University of Virginia)

Research Scientists, Postdoctral Scholars

• Yen-Thi Nguyen, Ph.D. (University of Iowa)

Graduate Students

• Xinlun Cheng (University of Virginia)
• Joseph Choi (University of Virginia)
• Irene Fang (University of Iowa)
• Ranabir Saha (University of Iowa)
• Pradeep Seshadri (University of Iowa)
• Dylan Walters (University of Iowa)

Undergraduate Students

• Austin Leonard (University of Iowa)
• William Kaiser (University of Virginia)
• Luke Weger (University of Iowa)

 

MaterIQ Partners & Sponsors

Federal Government

• Air Force Office of Scientific Research (AFOSR)
• Air Force Research Laboratory (AFRL)
• National Science Foundation (NSF)
• Army Research Office (ARO)

Industry

• We are seeking for industry research partners!

 

Follow Us on Social Media!

• ResearchGate: https://www.researchgate.net/project/MaterIQ
• Twitter: Coming soon!
• GitHub: Coming soon!