As an innovative nexus, the Lucy Family Institute for Data & Society recognizes the need for foundational expertise to prepare for the exciting potential that artificial intelligence has to revolutionize the manufacturing industry. In alignment with the National Science Foundation’s Artificial Intelligence (AI) Research Institutes, the Institute is announcing an AI & Materials initiative.
This initiative will support and launch multidisciplinary collaborations to transform the materials discovery landscape by enabling new AI-based capabilities, and by tailoring these discoveries to address societal challenges. An inaugural research project, “Leveraging Machine Learning for Materials Discovery,” has been announced, with principal investigator, Tengfei Luo, Dorini Family Professor of Aerospace and Mechanical Engineering for the College of Engineering. “I am honored to lead Lucy’s inaugural project on AI and Materials Discovery with Prof. Jiang. I believe this collaborative project will pave the way for a broader initiative, positioning Notre Dame as a leader in this emerging interdisciplinary field,” Luo said. He will be working with an interdisciplinary team of researchers to establish a framework for polymer informatics.
The AI & Materials initiative welcomes University of Notre Dame faculty researchers and industry stakeholders who have an interest in fostering the future of AI applications through the integration of AI with materials. Current research projects are listed below. Opportunities will be updated as they become available.
If you would like more information on how to become involved with the AI & Materials initiative, please contact us at email@example.com.
“Leveraging Machine Learning for Materials Discovery”
PI: Tengfei Luo, Dorini Family Professor, Aerospace and Mechanical Engineering
Co-PI: Meng Jiang, Associate Professor, Computer Science and Engineering
Collaborators: Nitesh Chawla and Xiangliang Zhang, Computer Science and Engineering
Ruilan Guo, Chemical and Biomolecular Engineering
Summary: Despite the prevalence of novel materials across diverse applications, demands to address emerging engineering and environmental challenges require new materials to possess unconventional features, such as multi-functionality and sustainability. Our ultimate vision is to establish a material discovery ecosystem that integrates data science, machine learning (ML), in-silico design, AI-guided robotic experimentation, and uncertainty quantification to enable materials-by-design. This proposed research aims to leverage our existing expertise in polymer (Luo) and ML (Jiang) to establish a framework for polymer informatics. We will focus on testing existing and creating new ML algorithms tailored for polymer chemistry to predict different properties, including thermal conductivity, mechanical strength and permeability. We also aim to establish a polymer database from molecular simulations and a repository collecting various ML models.
This project will support one postdoctoral associate for one year.