SI2-SSE: Automated Statistical Mechanics for the First-Principles Prediction of Finite Temperature Properties in Hybrid Organic-Inorganic Crystals

该项目旨在通过开发新的理论和计算工具来预测含有有机分子的复杂晶体材料的温度相关性质，从而提高材料科学的计算能力。最近发现有机-无机杂化化合物可以实现显着的光伏转换效率，这使得人们认识到迫切需要从根本上理解这些复杂化合物，并且第一性原理计算工具是必要的，以便能够预测它们的内在材料性质。有机-无机杂化化合物的室温性质受热激发的影响很大。因此，这些化合物的重要电子、热力学和动力学性质不能仅用量子力学方法直接预测，而需要考虑温度影响的统计力学工具。该项目的一个主要目标是开发高度自动化的统计力学软件工具，以预测材料性能，其中合金化、原子振动和分子旋转引起的无序被严格解释。这些工具将极大地提高根据第一性原理预测复杂材料性能的能力，从而能够定向设计广泛类别的新材料，这些材料应用于各种技术，包括能源转换和储存、碳捕获和有机电子。这项关于有机/无机杂化化合物的研究将产生基本的科学见解，这将导致宝贵的设计原则，使这些化合物能够进一步改进用于光伏应用。拟议的活动还将教育和培训计算材料科学方面的研究生，该领域在新材料的设计和快速实施方面越来越被认为是无价的。现代第一原理电子结构方法已经达到了惊人的精度和易用性水平，使它们成为新材料设计的无价工具。然而，电子结构方法本身并不能明确说明温度对热力学和动力学性质的影响。由于原子尺度的激发和无序产生的大的熵贡献，许多有希望用于能量存储和转换以及交通应用的材料的性能敏感地依赖于温度。大多数与技术相关的材料由于合金化而具有构型无序的特征，许多高温相被大的非简谐振动激励动态稳定。在一类新型的有机-无机复合钙钛矿材料中，熵对平衡和非平衡性质的贡献尤为重要，它们在光伏材料方面具有很大的应用前景。这些化合物属于一类晶体材料，可以在大的间隙笼子中容纳分子物种，并在室温下显示出广泛的原子和分子激发。通过在ABX3钙钛矿晶体的所有三个亚晶格上合金化，除了分子和振动激发外，还导致了构型无序，从而实现了最佳的光伏性能。因此，统计力学方法对于准确预测这些材料的电子、热力学和动力学性质是必不可少的。这个项目的目的是开发一个统计力学框架和一个伴随的高度自动化的软件基础设施，严格说明含有间隙分子物种的晶体固体中所有相关的构型、振动和分子自由度。有限温度、热力学和动力学性质的预测将依赖于有效的哈密顿量，这些哈密顿量用于在蒙特卡罗模拟中外推高精度的第一原理电子结构计算。该项目的一个主要活动是创建一个高度自动化的统计力学软件包，称为统计力学聚类法(CASM)，以根据第一原理预测多组分晶体材料的有限温度性质。将这些工具应用于合金化有机-无机复合钙钛矿的第一性原理研究，将使人们对各种原子和分子激发对电子结构、相稳定性和离子输运性质的相对重要性有一个基本的科学理解。

This project seeks to advance computational capabilities in materials science by developing new theoretical and computational tools to predict temperature dependent properties of complex crystalline materials containing organic molecules. The recent discovery that hybrid organic-inorganic compounds can achieve remarkable photovoltaic conversion efficiencies has led to the recognition that a fundamental understanding of these complex compounds is urgently needed and that first-principles computational tools are necessary to enable a prediction of their intrinsic materials properties. The room temperature properties of hybrid organic-inorganic compounds are strongly affected by thermal excitations. Important electronic, thermodynamic and kinetic properties of these compounds therefore cannot be predicted directly with quantum mechanical approaches alone, but require statistical mechanics tools that account for the effects of temperature. A major objective of this project is the development of highly automated statistical mechanics software tools to predict materials properties where disorder due to alloying, atomic vibrations and molecular rotations are rigorously accounted for. These tools will greatly enhance the ability to predict the properties of complex materials from first principles, thereby enabling the directed design of a broad class of new materials with applications in a wide variety of technologies, including energy conversion and storage, carbon capture and organic electronics. The fundamental scientific insights to be generated by this study on hybrid organic/inorganic compounds will lead to invaluable design principles to enable the further improvement of these compounds for photovoltaic applications. The proposed activity will also educate and train graduate students in computational materials science, a field that is increasingly recognized as invaluable in the design and rapid implementation of new materials.Modern first-principles electronic structure methods have reached a remarkable level of accuracy and ease of use, making them invaluable tools in the design of new materials. Electronic structure methods by themselves, however, do not explicitly account for the role of temperature on thermodynamic and kinetic properties. The properties of many promising materials for energy storage and conversion applications and for transportation applications depend sensitively on temperature due to large entropic contributions arising from atomic-scale excitations and disorder. Most materials of technological relevance are characterized by configurational disorder due to alloying and many high temperature phases are dynamically stabilized by large anharmonic vibrational excitations. Entropic contributions to equilibrium and non-equilibrium properties are especially important in a new class of hybrid organic-inorganic perovskites that show great promise as photovoltaic materials. These compounds belong to a class of crystalline materials that can host molecular species in large interstitial cages and exhibit a wide range of atomic and molecular excitations already at room temperature. Optimal photovoltaic properties are achieved by alloying on all three sublattices of the ABX3 perovskite crystal, leading to configurational disorder in addition to molecular and vibrational excitations. A statistical mechanics approach is therefore essential to accurately predict the electronic, thermodynamic and kinetic properties of these materials. The aim of this project is to develop a statistical mechanics framework and an accompanying highly automated software infrastructure that rigorously accounts for all relevant configurational, vibrational and molecular degrees of freedom in crystalline solids containing interstitial molecular species. The prediction of finite temperature thermodynamic and kinetic properties will rely on effective Hamiltonians that serve to extrapolate highly accurate first-principles electronic structure calculations within Monte Carlo simulations. A major activity of the project is the creation of a highly automated statistical mechanics software package called a Clusters Approach to Statistical Mechanics (CASM) to predict the finite temperature properties of multicomponent crystalline materials from first principles. The application of these tools in a first-principles study of alloyed hybrid organic-inorganic perovskites will generate a fundamental scientific understanding of the relative importance of the various atomic and molecular excitations on electronic structure, phase stability and ionic transport properties.