April 22 - 26, 2024
Seattle, Washington
May 7 - 9, 2024 (Virtual)
Symposium Supporters
2024 MRS Spring Meeting & Exhibit
MT03.06.03

Taming The Complexity of Materials Degradation with Machine Learning

When and Where

Apr 25, 2024
9:00am - 9:30am
Room 322, Level 3, Summit

Presenter(s)

Co-Author(s)

Brett Savoie1

Purdue University1

Abstract

Brett Savoie1

Purdue University1
Limited stability and unacceptable degradation products are common reasons for otherwise promising materials to fail technological translation. The enduring state-of-the-art for establishing these properties essentially remains make-and-break testing, which is costly and provides information only at the end of the materials development process. The complexity of the reaction networks that govern degradation and the difficulty of data analysis pose tremendous obstacles to predictive approaches, however recent advances in automated reaction prediction and machine learning are increasingly making it possible to tractably describe and even predict degradation phenomena. In this talk I will highlight our group’s recent work developing methods for predicting reaction outcomes and how they have been applied to several different materials classes. The second half of the talk will discuss machine learning approaches to the closely related problem of identifying degradation products on the basis of typical spectral information sources.

Symposium Organizers

Keith Butler, University College London
Kedar Hippalgaonkar, Nanyang Technological University
Shijing Sun, University of Washington
Jie Xu, Argonne National Laboratory

Symposium Support

Bronze
APL Machine Learning
SCIPRIOS GmbH

Session Chairs

Shijing Sun
Steven Torrisi

In this Session