DMSE Doctoral Thesis Defense

Deep Learning Methods for the Design and Understanding of Solid Materials

Tian Xie

Thursday, July 30, 2020 

10:00 – 11:00 AM EDT 

Contact dmse-gradoffice@mit.edu for participation link.

The trend of open material data and automation in the past decade offers a unique opportunity for data-driven design of novel materials for various applications as well as fundamental scientific understanding, but it also poses a challenge for conventional machine learning approaches based on structure features. In this thesis, we develop a class of deep learning methods that solve various types of learning problems for solid materials, and demonstrate its application to both accelerate material design and understand scientific knowledge. First, we present a neural network architecture to learn the representations of an arbitrary solid material, which encodes several fundamental symmetries for solid materials as inductive biases. Then, we extend the approach to explore four different learning problems: 1) supervised learning to predict material properties from structures; 2) visualization to understand structure-property relations; 3) unsupervised learning to understand atomic scale dynamics from time series trajectories; 4) active learning to explore an unknown material space. In each learning problem, we demonstrate the performance of our approach compared with previous approaches, and apply it to solve several realistic materials design problems and extract scientific insights from data.

 Thesis Supervisor

Jeffrey C. Grossman, Morton and Claire Goulder and Family Professor in Environmental Systems, Materials Science and Engineering, Massachusetts Institute of Technology 

Thesis Committee 

Rafael Gomez-Bombarelli, Toyota Assistant Professor in Materials Processing, Materials Science and Engineering, Massachusetts Institute of Technology  

Ju Li, Battelle Energy Alliance Professor, Nuclear Science and Engineering and Materials Science and Engineering, Massachusetts Institute of Technology 

Elsa A. Olivetti, Esther and Harold E. Edgerton Associate Professor, Materials Science and Engineering, Massachusetts Institute of Technology