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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李佳翰(Jia-Han Li) | |
dc.contributor.author | Tung-Ching HSIEH | en |
dc.contributor.author | 謝東景 | zh_TW |
dc.date.accessioned | 2021-06-16T02:36:18Z | - |
dc.date.available | 2022-07-31 | |
dc.date.copyright | 2020-09-16 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-19 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54006 | - |
dc.description.abstract | 新穎材料的開發能帶給人們許多好處與生活上的便利。傳統上尋找所需化學特性的新穎材料分子從開發至上市約須花費數年甚至數十年。為加速新穎材料開發,計算材料專家引入基於機器學習的反向設計方法,篩選目標材料有助於減少開發成本。本研究開發了一種遞迴神經網路(Recurrent neural networks, RNN)的化學變分自編碼模型(Chemical variational autoencoder, CVAE),並應用於研究鋰電池內的電解質材料的開發流程,了解不同物理性質與化學結構間之關聯性,我們從QM9分子資料庫篩選出符合特定性質之分子擔任訓練資料集,藉由模型訓練得到一個可生成化學結構與預測材料化學性質之深度學習模型,模型同時可確認其生成化學分子結構的有效性,使化學結構與化學特性彼此建立關聯性,讓模型具有對應的物理意義。對於生成分子的條件上,我們選擇最高佔據分子軌域能階、最低未佔據分子軌域能階、與其能隙等化學性質作為評估生成分子的化學穩定性的重要參數,介電系數參數作亦為抑制電解質內樹枝狀結構生成,而提升化學穩定性的重要參數。結果顯示,模型除了可生成符合特性之化學結構之外,並且藉由模型損失函數之權重的調整,我們的模型具有更準確與更有效率的生成化學結構效能。 | zh_TW |
dc.description.abstract | The discovery of novel materials brings enormous benefits and convenience to our life. To accelerate the exploration of novel materials, the deep-learning-based inverse design for the intelligent discovery of organic molecules was introduced by experts in computational materials. In our research, a chemical variational autoencoder (CVAE) designed by recurrent neural networks (RNN) was developed and applied to the development process of electrolyte materials in lithium-ion batteries. We screened out molecules with specific properties from the QM9 molecular database as the training data set. Through model training, we obtained a deep learning model that can generate chemical structures with high validity and predict the chemical properties. Besides, the model set up the relation between the chemical structures and desired chemical properties. Regarding the conditions for generating molecules, we selected important chemical properties as important parameters for evaluating the chemical stability for generating molecules purposefully, such as HOMO, LUMO, and HOMO-LUMO gap. Another parameter for chemical stability is the dielectric constant, which is for suppressing the formation of dendritic growth and improving chemical stability. The results showed that in addition to generating chemical structures that match the chemical properties, our model possessed more accurate and efficient ability on the generation performance chemical structures by adjusting the weight of the loss function of deep learning model. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T02:36:18Z (GMT). No. of bitstreams: 1 U0001-0408202011414200.pdf: 3164978 bytes, checksum: a690dd54bee157435a4762606d206dda (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 致謝 i 中文摘要 ii Abstract iii Contents iv List of Figure vi List of Table viii Chapter 1 Introduction 1 1.1 Energies and Batteries 1 1.2 Target properties of electrolyte materials 3 1.3 Motivation 6 Chapter 2 Relevant Theory 10 2.1 Representation of Molecules on Machine Learning 10 2.1.1 Description of Molecular Structures 10 2.1.2 Application of Natural Language Processing in Cheminformatics 11 2.2 Deep Learning 13 2.2.1 Artificial Neural Networks 13 2.2.2 Recurrent Neural Networks 16 2.2.3 Autoencoder and Variational Autoencoder 19 Chapter 3 Methods and Datasets 24 3.1 Application of Variational Autoencoder on Material Science 24 3.1.1 Reconstruction of Chemical Structures 26 3.1.2 Property Prediction 27 3.2 Datasets 29 Chapter 4 Results and Discussion 32 4.1 Performance for Reconstruction of Chemical Structures 32 4.2 Performance for Property Prediction 36 4.3 Design for Potential Molecules of Novel Electrolytes 47 Chapter 5 Conclusion 55 5.1 Conclusion 55 5.2 Future Work 56 Reference 57 | |
dc.language.iso | en | |
dc.title | 深層生成模型輔助鋰電池電解質之材料結構設計與性質預測 | zh_TW |
dc.title | Assisted Design of Chemical Structures for Electrolyte Materials in Li-ion Batteries and Properties Prediction via Deep Generative Model | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張瑞益(Ray-I Chang),李玟頡(Wen-Jay Lee),王耀群(Yao-Chun Wang),邱冠勳(Kuan-Hsun Chiu) | |
dc.subject.keyword | 變分自編碼器,鋰電池,電解質,化學穩定性, | zh_TW |
dc.subject.keyword | variational autoencoder,lithium-ion battery,electrolyte,chemical stability, | en |
dc.relation.page | 60 | |
dc.identifier.doi | 10.6342/NTU202002354 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-20 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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