Fireside Chat

Thursday February 29 12:30-1:30 – Conference Room A, Library (Large conference room on the second floor in the administrative area)

Accelerating Materials Design and Discovery Using Machine Learning 

 

Multiscale and Multiphysics Mechanics Lab 

Presenters: 

Sumanta Das, Associate Professor, Civil and Environmental Engineering || Mechanical, Industrial, and Systems Engineering

Bolaji Oladipo, Doctoral Candidate, Mechanical, Industrial, and Systems Engineering

Jonathan Villada, Doctoral Candidate, Civil and Environmental Engineering

Nausad Miyan, Doctoral Student, Civil and Environmental Engineering

Isaiah Narvaez, master’s student, Civil and Environmental Engineering

Abstract

Accelerating materials design and discovery is crucial to meet the rapidly evolving demands of technology and industry, enabling the development of smarter, more efficient, and sustainable solutions through novel engineering materials that display customizable and optimized properties. This interest is motivated either by specific application requirements or by a thoughtful scientific curiosity. Usually, the quest for these materials has been a laborious and costly endeavor, often relying on trial-and-error methods. It involves synthesizing and characterizing numerous compositions before arriving at the desired material. However, recent years have witnessed remarkable enhancements in this process through the adoption of high-throughput approaches. Leveraging advancements in artificial intelligence, particularly in machine learning adapted for materials science and engineering, data-driven materials prediction, and data-guided high-throughput characterization, has transformed the pace of materials design and discovery. These technological advancements now enable a significant acceleration in the identification and development of new materials.

Questions? Contact Drew Zhang, College of Business zhuzhang@uri.edu or Joan Peckham, Center for Computational Research – jpeckham@uri.edu