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

该奖项支持三元金属卤化物半导体的理论和计算研究。三元金属卤化物是一类新兴的材料，包括两种重金属（如银和铋）和卤化物原子（如碘）。本项目重点研究该家族的一组复杂晶体材料，这些材料具有柔性和廉价；此外，它们擅长将光转化为电，并被应用于太阳能电池、传感器和可穿戴电子产品中。尽管最初的实验结果很有希望，但三元金属卤化物是新的，它们的许多性质还没有很好地确定或理解。具体来说，目前尚不清楚结构和缺陷如何影响其在电气器件中的性能。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对三元金属卤化物的结构，电学和缺陷特性的原子水平的理解，并在这个家族中发现新的材料。该奖项还支持各种教育和外展活动。这项研究将包括对本科生进行计算材料建模和数据科学方法的培训。学生将参观合作研究机构，以获得额外的培训和经验。这个项目的成果将在太阳能电池和可再生能源相关工作的背景下进行，并在一个面向广大观众的研讨会上进行展示，包括高中教师、各年龄段的学生和公众。最后，将创建新的大学水平的机器学习和材料科学课程。该奖项支持三元金属卤化物离子半导体的理论和计算研究，使用密度泛函计算和数据科学方法相结合，以(i)确定Ag-Bi-I系统中导致光电器件性能瓶颈的原子级缺陷环境；（ii）通过创建数百个新系统的微观计算数据库，发现新的三元金属卤化物半导体。最近的实验研究已经产生了丰富的信息关于三元金属卤化物和光电子器件纳入他们。具体来说，新的太阳能电池采用了各种Ag-Bi-I化合物，这是Rudorffite类复杂半导体的一部分。PI将生成新的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被用来探索阳离子的各种排列，这些排列可能达到数十亿。利用生成的微观模型，PI将计算对电气设备性能建模有用的基本属性，如带隙和有效质量。此外，PI将探索候选体点缺陷和掺杂剂，表征它们的性质，并与现有实验进行比较。结果将被列入一个可搜索的数据库。将开发高通量和机器学习方法来加速生成准确的结果。该奖项还支持各种教育和外展活动。这项研究将包括对本科生进行计算材料建模和数据科学方法的培训。学生将参观合作研究机构，以获得额外的培训和经验。这个项目的成果将在太阳能电池和可再生能源相关工作的背景下进行，并在一个面向广大观众的研讨会上进行展示，包括高中教师、各年龄段的学生和公众。最后，将创建新的大学水平的机器学习和材料科学课程。该奖项反映了美国国家科学基金会的法定使命，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。

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.