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

SI2-SSE：用于有机-无机杂化晶体有限温度特性第一性原理预测的自动统计力学

• 批准号: 1642433
• 负责人: Anton Van der Ven
• 金额: $ 40.21万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642433&HistoricalAwards=false
• 关键词: SI2; SSE; Automated; Statistical; Mechanics

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.

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依赖性，从而提高材料科学的计算能力。最近发现，混合有机-无机化合物可以实现显着的光伏转换效率，导致认识到，这些复杂的化合物的基本理解是迫切需要的，第一原理计算工具是必要的，使其内在的材料性能的预测。有机-无机杂化化合物的室温性质受热激发的强烈影响。因此，这些化合物的重要电子、热力学和动力学性质不能单独用量子力学方法直接预测，而是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，以预测由于合金化，原子振动和分子旋转引起的无序的材料特性。这些工具将大大提高从第一原理预测复杂材料特性的能力，从而能够直接设计广泛的新材料，应用于各种技术，包括能量转换和储存，碳捕获和有机电子。这项关于混合有机/无机化合物的研究所产生的基本科学见解将导致宝贵的设计原则，以进一步改进这些化合物的光伏应用。拟议的活动还将教育和培训计算材料科学的研究生，这一领域越来越被认为是新材料设计和快速实现的宝贵领域。现代第一性原理电子结构方法已经达到了非常高的精度和易用性，使其成为新材料设计的宝贵工具。然而，电子结构方法本身并不能明确地解释温度对热力学和动力学性质的作用。由于原子尺度激发和无序产生的大熵贡献，许多用于能量存储和转换应用以及用于运输应用的有前途的材料的性质敏感地依赖于温度。大多数与技术相关的材料的特征在于由于合金化而导致的构型无序，并且许多高温相通过大的非谐振动激发而动态稳定。熵贡献的平衡和非平衡性能是特别重要的一类新的混合有机-无机钙钛矿，显示出巨大的希望作为光伏材料。这些化合物属于一类晶体材料，其可以在大的间隙笼中容纳分子物种，并且在室温下已经表现出广泛的原子和分子激发。通过在ABX 3钙钛矿晶体的所有三个子晶格上合金化来实现最佳光伏特性，从而导致除了分子和振动激发之外的构型无序。因此，统计力学方法是必不可少的，以准确地预测这些材料的电子，热力学和动力学性质。该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，严格占所有相关的配置，振动和分子的自由度在含有间隙分子物种的结晶固体。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，该哈密顿量用于在Monte Carlo模拟中推断高度精确的第一原理电子结构计算。该项目的一项主要活动是创建一个高度自动化的统计力学软件包，称为统计力学的聚类方法（CASM），用于从第一原理预测多组分晶体材料的有限温度特性。这些工具在合金混合有机-无机钙钛矿的第一性原理研究中的应用将产生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的基本科学理解。

项目成果

Finite-temperature simulation of anharmonicity and octahedral tilting transitions in halide perovskites

• DOI: 10.1103/physrevmaterials.3.113605
• 发表时间: 2019-11-18
• 期刊: PHYSICAL REVIEW MATERIALS
• 影响因子: 3.4
• 作者: Bechtel, Jonathon S.;Thomas, John C.;Van der Ven, Anton
• 通讯作者: Van der Ven, Anton

MultiShifter: Software to generate structural models of extended two-dimensional defects in 3D and 2D crystals

MultiShifter：用于生成 3D 和 2D 晶体中扩展二维缺陷结构模型的软件

• DOI: 10.1016/j.commatsci.2021.110310
• 发表时间: 2021
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Goiri, Jon Gabriel;Van der Ven, Anton
• 通讯作者: Van der Ven, Anton

Machine learning the density functional theory potential energy surface for the inorganic halide perovskite CsPbBr3

• DOI: 10.1103/physrevb.100.134101
• 发表时间: 2019-07
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: John C. Thomas;J. S. Bechtel;A. Natarajan;A. Van der Ven

Hamiltonians and order parameters for crystals of orientable molecules

可取向分子晶体的哈密顿量和有序参数

• DOI: 10.1103/physrevb.98.094105
• 发表时间: 2018
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: Thomas, John C.;Bechtel, Jonathon S.;Van der Ven, Anton
• 通讯作者: Van der Ven, Anton

Controlling the Electrochemical Properties of Spinel Intercalation Compounds

• DOI: 10.1021/acsaem.8b01080
• 发表时间: 2018-10
• 期刊: ACS Applied Energy Materials
• 影响因子: 6.4
• 作者: S. Kolli;A. Van der Ven