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Deep learning methods for the design and understanding of solid materials

Author(s)
Xie, Tian,Ph.D.Massachusetts Institute of Technology.
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Other Contributors
Massachusetts Institute of Technology. Department of Materials Science and Engineering.
Advisor
Jeffrey Grossman.
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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. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
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.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2020
 
Cataloged from student-submitted PDF of thesis.
 
Includes bibliographical references (pages 127-145).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/129054
Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Publisher
Massachusetts Institute of Technology
Keywords
Materials Science and Engineering.

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