Accelerating the Computation and Design of Nanoscale Materials with Deep Learning
In this article-based thesis, we cover applications of deep learning to different problems in condensed matter physics, where the goal is to either accelerate the computation or design of a nanoscale material. We first motivate and introduce how machine learning methods can be used to accelerate tra...
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| Format: | Others |
| Language: | en |
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Université d'Ottawa / University of Ottawa
2021
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| Online Access: | http://hdl.handle.net/10393/42988 http://dx.doi.org/10.20381/ruor-27205 |