This article uses machine learning to predict Slater-Koster method’s parameters We train a machine to map the calculated Hamiltonian to Slater-Koster parameters And we use these predicted parameters to construct Slater-Koster Hamiltonian In chapter 2 I simply introduce some Physics knowledges and deep learning In this part I mainly talk about how deep learning work In chapter 3 I introduce the tight-binding method deep learning again and unsupervised learning K-means In this part I talk about how to train a deep learning and how to make it perform better K-means is included because I need use it to cluster some data In chapter 4 material Wse2 and material Sb2Te3 are used as my example First I tried many deep learning models and techniques to predict WSe2’s Slater-Koster parameters But there are still some parameters that deep learning cannot predict Thus I cluster them with K-means method to find the nearest parameters Next I used the same procedure to find the Sb2Te3’s parameters Although the results show that we can use machine learning to find Slater-Koster parameters there are still some results that need improvement Full procedure is two steps we can’t predict all parameters just use one machine learning tool When the calculated Hamiltonian is non-Slater-Koster Hamiltonian there will be some deviations in the predicted results

Date of Award | 2020 |
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Original language | English |
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Supervisor | Tay-Rong Chang (Supervisor) |
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Use Machine Learning to Find Slater-Koster Method's Parameters

世豪, 黃. (Author). 2020

Student thesis: Doctoral Thesis