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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80992完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張建成(Chien-Cheng Chang) | |
| dc.contributor.author | Xuan Li | en |
| dc.contributor.author | 李宣 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:25:13Z | - |
| dc.date.available | 2021-09-11 | |
| dc.date.available | 2022-11-24T03:25:13Z | - |
| dc.date.copyright | 2021-09-11 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-03 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80992 | - |
| dc.description.abstract | "近年來綠色能源是全世界都想要探索的方向，為了擺脫化石燃料，科學家往水力、風力、太陽能等不同的能量來轉換成作為發電的能量來源，而鈣鈦礦(Perovskite)是最近具有很大潛力的太陽能電池材料之一，其主要優點是優秀的光電轉換效率，被視為傳統的矽太陽能電池的接班者，其最大的優勢是製程上不需要像矽一樣做半導體製程，可大幅降低其製造成本，在商用上有很大的發展空間。 鈣鈦礦的晶格結構為ABX3，為了使光電效率提升且同時擁有更好的穩定性，現在的鈣鈦礦已經不再只是單一化合物，而是摻雜多種化學成分所組成的混摻物，在調整混合比例的過程中，微觀尺度的材料性質會有很大的改變，受限於量測技術和時間成本上，很難單靠實驗得到結果，純粹使用試誤法將會曠日費時且效果不彰。因此，吾人嘗試藉由原子尺度模擬以釐清其中各種因素。 第一原理計算(Ab initio calculation)是建立於量子力學理論下所做理論推導的模擬，以計算薛丁格方程式求出電子密度分佈來獲得整個系統的物理性質，其特色是精確度高，且不需要先經過實驗來得到材料之物理性質，所付出的代價是計算時間長，且需要大量的計算資源。故本研究將藉由第一原理計算的結果，使用人工神經網路建立擁有七種元素(C, H, N, Cs, Pb, I, Br)之鈣鈦礦勢能模型，以利快速計算出其他不同比例之鈣鈦礦物理性質，且計算不會因尺寸大小所侷限，該神經網路之勢能模型可以計算出系統性質以及結構優化，受惠於此模型計算速度比第一原理計算快速許多，故吾人可以計算範圍內所有不同摻雜比例的鈣鈦礦，藉此找出最穩定之摻雜比例。 最後，吾人將以貝葉斯優化法來找出特定摻雜比例，在此研究搜索目標為混合能，混合能可以判斷系統是否容易有分相的情況，以該方法可以在全比例中快速搜索出想要的比例。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:25:13Z (GMT). No. of bitstreams: 1 U0001-0209202115034100.pdf: 5954205 bytes, checksum: 989567e12d9e01ba9925c71f1da8cfbf (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "誌謝 I 摘要 II ABSTRACT III 目錄 V 圖目錄 VIII 表目錄 XI 第一章 緒論 1 1.1 前言 1 1.2 鈣鈦礦結構介紹 3 1.2.1 鈣鈦礦結構 3 1.2.2 鈣鈦礦原子種類 4 1.2.3 製程方式 6 1.2.4 鈣鈦礦太陽能性能 8 1.3 研究動機 9 1.4 文獻回顧 10 第二章 理論介紹與計算方法 15 2.1 第一原理方法 15 2.1.1 前言 15 2.1.2 薛丁格方程式 15 2.1.3 波恩-歐本海默近似(Born-Oppenheimer approximation) 17 2.1.4 密度泛函理論(Density Functional Theory, DFT) 19 2.1.5 局部密度近似(Local Density Approximation, LDA) 22 2.1.6 廣義梯度近似(Generalized Gradient Approximation, GGA) 23 2.1.7 贋勢(Pseudopotential) 23 2.1.8 布洛赫定理(Bloch’s theorem) 24 2.1.9 自洽計算(Self-consistency) 26 2.2 人工神經網路 28 2.2.1 人工神經網路簡介 28 2.2.2 人工神經元模型 30 2.2.3 監督式學習(Supervised learning) 32 2.2.4 前饋式神經網路(Feedforward Neural Network) 33 2.2.5 反向傳播演算法(Backpropagation Algorithm) 34 2.2.6 高斯描述符(Gaussian descriptor) 38 2.3 其他 41 2.3.1 統計模型 (系綜) 41 2.3.2 貝葉斯優化(Bayesian optimization) 42 第三章 模擬流程與計算架構 47 3.1 模擬流程 47 3.1.1 鈣鈦礦結構建模 48 3.1.2 VASP結構優化 (Structure optimization ) 50 3.1.3 神經網路訓練 52 3.1.4 ANN勢能計算 55 3.2 混合能計算 56 3.2.1 貝葉斯優化過程 56 3.2.2 混合能計算 59 第四章 結果與討論 61 4.1 簡介 61 4.2 生成結構 61 4.3 VASP結構優化 64 4.4 ANN訓練 69 4.5 混合能 76 第五章 結論與未來展望 78 5.1 結論 78 5.2 未來展望 79 參考文獻 80" | |
| dc.language.iso | zh-TW | |
| dc.subject | 混合能 | zh_TW |
| dc.subject | 人工神經網路 | zh_TW |
| dc.subject | 分子勢能 | zh_TW |
| dc.subject | 第一原理 | zh_TW |
| dc.subject | 鈣鈦礦 | zh_TW |
| dc.subject | 貝葉斯優化 | zh_TW |
| dc.subject | Molecular potential | en |
| dc.subject | Artificial neural network | en |
| dc.subject | Perovskite | en |
| dc.subject | Bayesian optimization | en |
| dc.subject | Mixing energy | en |
| dc.subject | Ab initio calculation | en |
| dc.title | 人工神經網路於複雜鈣鈦礦材料之結構預測 | zh_TW |
| dc.title | Artificial neural network applied in the structure prediction of complex perovskites | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 包淳偉(Chun-Wei Pao) | |
| dc.contributor.oralexamcommittee | 牛仰堯(Hsin-Tsai Liu),張家歐(Chih-Yang Tseng),趙聖德 | |
| dc.subject.keyword | 鈣鈦礦,第一原理,分子勢能,人工神經網路,貝葉斯優化,混合能, | zh_TW |
| dc.subject.keyword | Perovskite,Ab initio calculation,Molecular potential,Artificial neural network,Bayesian optimization,Mixing energy, | en |
| dc.relation.page | 87 | |
| dc.identifier.doi | 10.6342/NTU202102956 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-06 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 應用力學研究所 | zh_TW |
| 顯示於系所單位： | 應用力學研究所 | |
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