Intelligent design and discovery of novel 2D TMD alloys with machine learning

2D transition metal dichalcogenides (TMDs) are a novel class of nanomaterials with interesting properties due to their confined dimensions, distinct from their bulk counterpart. This enables fine-tuning of electronic and optical properties that can be utilized in various applications. However, the d...

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Main Author: Tan, Nicholas
Other Authors: Liu Zheng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/165736
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1657362023-04-15T16:45:49Z Intelligent design and discovery of novel 2D TMD alloys with machine learning Tan, Nicholas Liu Zheng School of Materials Science and Engineering Z.Liu@ntu.edu.sg Engineering::Materials 2D transition metal dichalcogenides (TMDs) are a novel class of nanomaterials with interesting properties due to their confined dimensions, distinct from their bulk counterpart. This enables fine-tuning of electronic and optical properties that can be utilized in various applications. However, the discovery of new nanomaterials with suitable properties is time-consuming and requires significant research effort. The purpose of this project aims to reduce the discovery time of new TMDs using machine learning techniques and predict important properties of these materials to aid in the synthesis of novel TMDs. Important properties and features of 2D TMDs were first collected from online databases before undergoing data cleaning, which were then used for model selection and training. The models utilised were extreme gradient boosting (XGB), multilayer perceptron (MLP), and support vector machine (SVM). Continuous improvements of the models were also done through feature engineering by looking at the feature importance of each model. Model evaluation was then done by looking at its R2, r, and MSE values. This project successfully trained five models to predict the target properties of Heat of Formation (HOF), Energy Above Convex Hull (EACH), Bandgap (BG), Direct Bandgap (DBG), and Magnetic Moment (MM). The MLP model was shown to be the best model for HOF, BG, and DBG, while the XGB model was the best model for EACH and MM, based on their R2 values. Utilising both models, we successfully predicted the properties of three TMD/TMD-alloys which were not present in the dataset. Specifically, the MLP model was used for BG prediction, and the XGB model was used for HOF prediction. In summary, successful machine learning models were created to predict five important targeted properties of TMD/TMD-alloys materials. The models were able to predict the HOF and BG of materials not found in the dataset. However, the model accuracy could be further improved by increasing the dataset, choosing more features, or improving on the chosen models. Bachelor of Engineering (Materials Engineering) 2023-04-10T09:14:28Z 2023-04-10T09:14:28Z 2023 Final Year Project (FYP) Tan, N. (2023). Intelligent design and discovery of novel 2D TMD alloys with machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/165736 https://hdl.handle.net/10356/165736 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Materials
spellingShingle Engineering::Materials
Tan, Nicholas
Intelligent design and discovery of novel 2D TMD alloys with machine learning
description 2D transition metal dichalcogenides (TMDs) are a novel class of nanomaterials with interesting properties due to their confined dimensions, distinct from their bulk counterpart. This enables fine-tuning of electronic and optical properties that can be utilized in various applications. However, the discovery of new nanomaterials with suitable properties is time-consuming and requires significant research effort. The purpose of this project aims to reduce the discovery time of new TMDs using machine learning techniques and predict important properties of these materials to aid in the synthesis of novel TMDs. Important properties and features of 2D TMDs were first collected from online databases before undergoing data cleaning, which were then used for model selection and training. The models utilised were extreme gradient boosting (XGB), multilayer perceptron (MLP), and support vector machine (SVM). Continuous improvements of the models were also done through feature engineering by looking at the feature importance of each model. Model evaluation was then done by looking at its R2, r, and MSE values. This project successfully trained five models to predict the target properties of Heat of Formation (HOF), Energy Above Convex Hull (EACH), Bandgap (BG), Direct Bandgap (DBG), and Magnetic Moment (MM). The MLP model was shown to be the best model for HOF, BG, and DBG, while the XGB model was the best model for EACH and MM, based on their R2 values. Utilising both models, we successfully predicted the properties of three TMD/TMD-alloys which were not present in the dataset. Specifically, the MLP model was used for BG prediction, and the XGB model was used for HOF prediction. In summary, successful machine learning models were created to predict five important targeted properties of TMD/TMD-alloys materials. The models were able to predict the HOF and BG of materials not found in the dataset. However, the model accuracy could be further improved by increasing the dataset, choosing more features, or improving on the chosen models.
author2 Liu Zheng
author_facet Liu Zheng
Tan, Nicholas
format Final Year Project
author Tan, Nicholas
author_sort Tan, Nicholas
title Intelligent design and discovery of novel 2D TMD alloys with machine learning
title_short Intelligent design and discovery of novel 2D TMD alloys with machine learning
title_full Intelligent design and discovery of novel 2D TMD alloys with machine learning
title_fullStr Intelligent design and discovery of novel 2D TMD alloys with machine learning
title_full_unstemmed Intelligent design and discovery of novel 2D TMD alloys with machine learning
title_sort intelligent design and discovery of novel 2d tmd alloys with machine learning
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/165736
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