Deep learning methods for the design and understanding of solid materials

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, I 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, I 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, I 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, I demonstrate the performance of the approach compared with previous approaches, and apply it to solve several realistic materials design problems and extract scientific insights from data.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Materials Science and Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Xie, Tian,Ph.D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Jeffrey Grossman.

• Materials Science and Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129054

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129054