Organic-inorganic lead-halide perovskite material has attracted significant attention from numerous research groups due to its favorable properties when deployed as an active layer within wide range of optoelectronic and photovoltaic devices. To this day, however, its main drawback, insufficient environmental stability has not yet been completely solved. In the present text the study of alternative variations of perovskite structure is presented, namely, all-inorganic antimony-based and 2D Ruddlessden-Popper types, which are stable against the elements. By employing large scale ab-initio density functional theory (DFT) calculations, we explored the influence of hydrogen bonding formation on shape of the 2D perovskite single crystal and studied the intertwined defect present in 2D perovskite material. In addition, we explored the influence of different adduct molecules on subsequent stability of the all-inorganic antimony-based perovskite films. Hence, we explained processing-structure-property (PSP) relationship by means of ab-initio DFT, which is a necessary prerequisite for improving the morphology of the film and, therefore, device efficiency. To tackle the spatial length scale limitations of conventional DFT calculations, we leveraged the power of machine learning and successfully trained an accurate and robust potential energy model of 2D perovskite using the SNAP scheme. This model enabled us to perform a series of large-scale hybrid molecular dynamics/Monte Carlo simulations to investigate the layer distribution of 2D perovskite of a variety of stoichiometries and spacers and verify the experimental findings. This demonstrated that machine learning can play an ever-increasing role in studying the microstructure of complex perovskite materials.

Abstract i
中文摘要 ii
Contents iii
Chapter 1 Introduction 1
1.1 Rationale 1
1.2 Structure and properties of organic-inorganic lead-halide perovskite 2
1.3 Structure and properties of organic-inorganic lead-halide 2D Ruddlesden-Popper perovskite 7
1.4 Structure and properties of lead-free inorganic Sb-based perovskite structure 11
1.5 Overview of the thesis 13
Chapter 2 Literature review 16
2.1 2D Ruddlesden-Popper perovskite characterization 16
2.2 2D perovskite morphology 18
2.3 Impact of morphology on device performance 22
Chapter 3 Computational methods 24
3.1 Density Functional Theory 24
3.1.1 Schrodinger equation 24
3.1.2 Thomas-Fermi model 25
3.1.3 The Hohenberg-Kohn theorems 26
3.1.4 Kohn-Sham theory 27
3.1.5 Concepts in DFT 29
3.1.5.1 The exchange-correlation energy 29
3.1.5.2 Self-consistency iterations for KS equations flowchart 31
3.1.5.3 Pseudopotentials 32
3.1.5.4 Electrons in periodic potential 33
3.1.5.5 Ab-initio molecular dynamics 34
3.2 Machine learning of interatomic potentials 35
3.2.1 Descriptors 37
3.2.2 Machine learning potentials implementations 38
3.2.2.1 Neural networks 38
3.2.2.2 Gaussian approximated potentials 39
3.2.3 SNAP potential 40
3.2.3.1 Mathematical formulation 40
3.2.3.2 SNAP potential training flowchart 43
3.3 Monte Carlo method 44
3.3.1 Metropolis algorithm 45
3.3.1.1 Hybrid Metropolis Monte Carlo approach 47
Chapter 4 Lewis base adduct molecule absorption on a Pb-free all Cs3Sb2I9 inorganic perovskite 49
4.1 Introduction 49
4.2 Methodology 50
4.3 Results and Discussion 53
4.4 Summary and outlook 59
Chapter 5 Surface structures and equilibrium shapes of layered 2D Ruddlesden-Popper perovskite crystals from density functional theory calculations 60
5.1 Introduction 60
5.2 Methodology 61
5.3 Results and discussion 64
5.4 Summary and outlook 78
Chapter 6 Structural and electronic properties of intertwined defect in Ruddlesden−Popper 2D perovskites study using density functional theory calculations 80
6.1 Introduction 80
6.2 Computational method 81
6.3 Results and Discussion 84
6.3.1 Formation energy of intertwined structure 84
6.3.2 Band gap 89
6.3.3 Band structure 92
6.3.4 Density of states 94
6.4 Conclusion 98
6.5 Summary and outlook 99
Chapter 7 Layer distribution study in 2D Ruddlesden-Popper perovskites using SNAP potential and Monte Carlo simulations 101
7.1 Introduction 101
7.2 Results and discussion 103
7.2.1 SNAP potential training 103
7.2.2 SNAP Potential validation 107
7.2.3 Layer distribution from Monte Carlo simulations 109
7.4 Summary and Outlook 119
Chapter 8 Summary 121
8.1 Conclusion 121
8.2 Future prospects 124
Bibliography 127

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