醣類分子在生物體的各種化學反應中扮演著不可或缺的角色，理解其三維結構對於闡明其作用與功能是至關重要的。然而，由於其分子具有高度活動的特性，造成其複雜的構型空間不易進行高效且準確的取樣，即使對於單醣或雙醣的探索也充滿挑戰性。本文提出了一種結合結構取樣演算法和神經網絡勢能（NNPs）的計算方案來克服上述挑戰。NNPs 兼顧了半經驗方法的效率和第一原理方法的準確性，能準確有效地探索單醣或雙醣的構型空間。 本方法訓練出的NNPs 可達到高準確度，其誤差小於 4 kJ/mol，能有效地識別各種醣分子的穩定構型，並與實驗數據進行比較。本計算方案為研究重要生物分子的結構特性提供了一個強大的工具，有助於更好地理解醣分子構型，並為未來研究其功能和應用鋪平了道路。

Saccharides play crucial roles in various biological processes, and understanding their three-dimensional structures is essential for elucidating their functions and interactions. However, even for mono- or di-saccharides, efficient and accurate sampling through their complex conformational space is challenging due to their highly flexible nature. This thesis presents a computational scheme that combines structure sampling algorithms and neural network potentials (NNPs) to overcome above-mentioned challenges. NNPs are used to synergize the efficiency of semi-empirical methods and accuracy of first-principles methods to efficiently explore the conformational space of mono-saccharides and di-saccharides while keeping the first-principles accuracy. The developed NNPs reach a respectable level of accuracy (better than 4 kJ/mol) and enable efficient identification of low-energy conformers to compare with experimental data. This comprehensive computational approach offers a powerful tool for investigating the structural properties of these biologically important molecules, contributing to a better understanding of saccharide conformations and paving the way for future studies on their functions and applications.

Contents
Abstract (Chinese) I
Abstract II
Contents III
List of Figures VI
List of Tables XVI

1 Introduction --------------------------------------------------- 1

2 A first-principles exploration of the conformational space of sodi-
ated pyranose assisted by neural network potentials -------------- 8
2.1 Introduction ------------------------------------------------- 9
2.2 Methodology and computational details ------------------------ 10
2.2.1 Extracted data from the geometry optimizations at the B3LYP
level ------------------------------------------------------------ 10
2.2.2 Building a neural network potential model with SchNet ------ 12
2.2.3 An active learning scheme ---------------------------------- 18
2.2.4 The structure sampling algorithms mono-saccharides at DFTB3- 19
2.3 Results and discussion --------------------------------------- 21
2.3.1 Summary of the NNP predictive performance on hexoses ------- 21
2.3.2 Application of the NNP models to find new structures ------- 25
2.3.3 Comparison of the local minima by NNP and by B3LYP --------- 27
2.3.4 Analysis of the energetic and structural diversity in the struc-
tural database at B3LYP ------------------------------------------ 29
2.4 Conclusions -------------------------------------------------- 33

3 A first-principles exploration of the conformational space of sodi-
ated di-saccharides assisted by semi-empirical methods and neural
network potentials ----------------------------------------------- 34
3.1 Introduction ------------------------------------------------- 35
3.2 Methodology and computational details ------------------------ 37
3.2.1 Nomenclature ----------------------------------------------- 37
3.2.2 Structural sampling scheme for di-saccharide --------------- 37
3.2.3 Data generation and NNP training --------------------------- 41
3.2.4 Integration of DFTB3, NNP, and M062X in the structure searching scheme of di-saccharides ----------------------------------------- 44
3.2.5 Quantum Harmonic Superposition Approximation --------------- 46
3.3 Results and discussion --------------------------------------- 47
3.4 Conclusions -------------------------------------------------- 63

4 Unravelling the low-energy conformers of neutral and charged mono- and di-saccharides with first-principles accuracy assisted by neural network potentials ----------------------------------------------- 65
4.1 Introduction ------------------------------------------------- 66
4.2 Methodology and computational details ------------------------ 67
4.2.1 Structure sampling scheme ---------------------------------- 67
4.2.2 Preparing data for NNP model training ---------------------- 70
4.3 Results and discussion --------------------------------------- 72
4.3.1 The predictive performance of NNP model -------------------- 72
4.3.2 Employment of the NNP models to identify low-energy con
-formers --------------------------------------------------------- 75
4.3.3 Structure analysis of neutral structures ------------------- 78
4.3.4 Vibrational spectra analysis of mono-saccharides ----------- 79
4.3.5 Utilization of mono-saccharide structure database for effi-
cient sampling of protonated di-saccharides ---------------------- 87
4.4 Conclusions -------------------------------------------------- 93

5 Conclusions and Future works ----------------------------------- 95

Bibliography ----------------------------------------------------- 99

Appendix A Unravelling the low-energy conformers of neutral and charged mono- and di-saccharides with first-principles accuracy assisted by neural network potentials ---------------------------- 117

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