RUI: CMMT: Computational Study of Ternary Metal Halides for Optoelectronics: Structural, Electrical and Defect Properties

RUI：CMMT：光电子学用三元金属卤化物的计算研究：结构、电气和缺陷特性

• 批准号: 2127473
• 负责人: Blair Tuttle
• 金额: $ 18.42万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127473&HistoricalAwards=false
• 关键词: RUI; CMMT; Computational; Study; Ternary

NONTECHNICAL SUMMARY This award supports theoretical and computational research on ternary metal halide semiconductors. Ternary metal halides are an emerging class of materials including two heavy metals (e.g. silver and bismuth) and halide atoms (e.g. iodine). This project focuses on a set of complex crystalline materials in this family which are flexible and cheap; in addition, they are good at converting light into electricity and are being incorporated in solar cells, sensors and wearable electronics. Despite promising initial experimental results, ternary metal halides are new and many of their properties are not well established or understood. Specifically, it is unclear how structure and defects affect their performance in electrical devices. In this project, the PI and his team will use computational materials modeling and data science methods to develop an atomic level understanding of the structural, electrical, and defect properties of ternary metal halides and to discover new materials in this family. This awards also supports various educational and outreach activities. The research will involve training of undergraduate students in computational materials modeling and data science methods. The students will visit collaborating research institutions to gain additional training and experience. The results from this project will be placed in the context of related work on solar cells and renewable energy, and presented in a workshop accessible to a wide audience, including high school teachers, students of all ages, and the general public. Finally, new college-level curriculum on machine learning and materials science will be created. TECHNICAL SUMMARY This award supports theoretical and computational research on ternary metal halide ionic semiconductors using a combination of density functional calculations and data-science methods to (i) identify the atomic-level defect environments in the Ag-Bi-I system that are responsible for bottlenecks in their optoelectronic device performance, and (ii) discover new ternary metal halide semiconductors by creating a database of microscopic-level calculations for hundreds of new systems.Recent experimental studies have produced a wealth of information regarding ternary metal halides and the optoelectronic devices incorporating them. Specifically, new solar cells employ various Ag-Bi-I compounds, which are part of the Rudorffite class of complex semiconductors. The PI will generate new microscopic models of Ag-Bi-I that will match the experimentally determined range of stoichiometries. New random forest modeling simulations will be employed to explore the various arrangements of cations, which can run into the billions. Using the generated microscopic models, the PI will calculate basic properties useful for modeling electrical device performance such as band gaps and effective masses. In addition, the PI will explore candidate bulk point defects and dopants, characterize their properties, and compare to available experiments. The results will be tabulated into a searchable database. High throughput and machine learning methods will be developed to accelerate the generation of accurate results. This awards also supports various educational and outreach activities. The research will involve training of undergraduate students in computational materials modeling and data science methods. The students will visit collaborating research institutions to gain additional training and experience. The results from this project will be placed in the context of related work on solar cells and renewable energy, and presented in a workshop accessible to a wide audience, including high school teachers, students of all ages, and the general public. Finally, new college-level curriculum on machine learning and materials science will be created.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持三元金属卤化物半导体的理论和计算研究。三元金属卤化物是包括两种重金属（例如，银和铋）和卤化物原子（例如，碘）的新兴类别的材料。该项目的重点是该家族中的一组复杂晶体材料，这些材料灵活且便宜;此外，它们擅长将光转化为电，并被纳入太阳能电池，传感器和可穿戴电子产品中。尽管初步实验结果令人鼓舞，三元金属卤化物是新的，它们的许多性质还没有很好地建立或理解。具体而言，目前还不清楚结构和缺陷如何影响其在电气设备中的性能。在这个项目中，PI和他的团队将使用计算材料建模和数据科学方法来开发三元金属卤化物的结构，电学和缺陷特性的原子水平理解，并发现这个家族中的新材料。该奖项还支持各种教育和外联活动。该研究将涉及对本科生进行计算材料建模和数据科学方法的培训。学生将访问合作研究机构，以获得额外的培训和经验。该项目的成果将放在太阳能电池和可再生能源的相关工作中，并在一个讲习班上介绍，讲习班面向广泛的受众，包括高中教师、各年龄段的学生和公众。最后，将创建关于机器学习和材料科学的新的大学课程。该奖项支持使用密度泛函计算和数据科学方法的组合对三元金属卤化物离子半导体进行理论和计算研究，以（i）识别Ag-Bi-I系统中的原子级缺陷环境，这些缺陷环境是其光电器件性能瓶颈的原因，以及（ii）通过创建微观-最近的实验研究已经产生了关于三元金属卤化物和三元金属卤化物的大量信息。光电器件结合它们。具体来说，新的太阳能电池采用各种Ag-Bi-I化合物，它们是Rudorffite类复杂半导体的一部分。PI将产生新的Ag-Bi-I微观模型，该模型将与实验确定的化学计量范围相匹配。新的随机森林模型模拟将用于探索阳离子的各种排列，这些排列可能达到数十亿。使用生成的微观模型，PI将计算可用于建模电气器件性能的基本属性，例如带隙和有效质量。此外，PI将探索候选体点缺陷和掺杂剂，表征其特性，并与现有实验进行比较。结果将编入可检索的数据库。将开发高通量和机器学习方法，以加速准确结果的生成。该奖项还支持各种教育和推广活动。该研究将涉及对本科生进行计算材料建模和数据科学方法的培训。学生将访问合作研究机构，以获得额外的培训和经验。该项目的成果将放在太阳能电池和可再生能源的相关工作中，并在一个讲习班上介绍，讲习班面向广泛的受众，包括高中教师、各年龄段的学生和公众。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。