RII Track-4: Optimizing the Chemistry of Heterointerfaces in Photovoltaics: A Combination of Electronic Structure Calculations and Machine Learning Approach

材料的性质通常与其内部界面的原子结构和化学成分有关。在内部界面中，异质界面是将具有不同原子结构和化学结构的两种材料分开的界面。这些异质界面的原子结构和化学成分很复杂，很难从形成异质界面的个别材料中预测出来。这种复杂性在多组分异质界面中呈指数级增加，因为它涉及界面上的大量化学可能性。因此，设计一种具有目标材料性质的新型多组分异质界面的化学结构是一项具有挑战性的任务。该研究项目旨在利用电子结构计算和机器学习相结合的方法，提高我们目前的能力，以确定光伏应用中具有目标电子性质的多组分异质界面的化学组成。机器学习工具可以有效地提取隐藏的化学-性质关系，从而大大减少识别满足所需电子性质需求的异质界面的化学成分所需的时间，这是加速发现新型异质界面的关键因素。拟议的项目解决了联邦政府对材料基因组计划的重要任务，其目标是大幅减少发现、制造和部署先进材料的时间和成本。多组分异质界面长期以来一直引起材料科学家和物理学家的兴趣，部分原因是它们的原子、电子结构和化学非常复杂。这种复杂性使得设计具有目标材料属性的新型多组分异质界面成为一项艰巨的任务。这是因为在界面上导航多个元素之间的巨大组合、化学和构型可能性实在太大了。该研究项目旨在利用电子结构计算和机器学习相结合的方法来设计具有目标电子性质的光伏应用的异质界面的化学组成。更具体地说，PI计划(A)深入了解使用电子结构计算确定异质界面的电子和原子结构的基本物理；以及(B)应用机器学习工具探索界面隐藏的化学-性质关系，预测所需电子性质的异质界面的化学。所提出的方法不同于传统的爱迪生试验-测试实验周期的费时和昂贵的方法；因此，它可以大大加快材料发现的速度。PI预计，在该项目完成后，将产生一个通用的计算模板，用于研究高度复杂的多组分异质界面的结构-化学-性质关系。最后，机器学习将是未来材料发现不可或缺的一部分。从这个项目中获得的知识将帮助培训学生(S)关于机器学习工具及其在材料研究中的效用，增加他们在职业生涯早期对这个不断增长和有影响力的材料科学领域的接触。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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Material properties are often linked to the atomic structure and chemistry of its internal interfaces. Among the internal interfaces, heterointerfaces are boundaries separating two materials with different atomic structure and chemistry. The atomic structure and chemistry of these heterointerfaces are complex and cannot be easily predicted from the individual materials that form the heterointerface. This complexity increases exponentially in multi-component heterointerfaces as it involves a vast number of chemical possibilities at the interface. Thus, designing the chemistry of a novel multi-component heterointerface with a targeted material property is a challenging task. This research project aims to enhance our current capability to determine the chemistry of a multi-component heterointerface with a targeted electronic property for photovoltaic applications using a combination of electronic structure calculations and machine learning approach. Machine learning tools can substantially reduce the time needed to identify the chemistry of a heterointerface that meets a desired electronic property need by efficiently extracting hidden chemistry-property relationships, a key factor toward accelerated discovery of novel heterointerface. The proposed project addresses the important federal government mandate of Materials Genome Initiative, the objective of which was to substantially reduce the time and cost to discover, manufacture, and deploy advanced materials.Multicomponent heterointerfaces have long intrigued materials scientists and physicists, in part, because of the sheer complexity of their atomic and electronic structure and chemistry. Such complexity can make designing a novel multi-component heterointerface with a targeted material property a non-trivial task. This is because navigating the vast combinatorial chemical and configurational possibilities between multiple elements at the interface is simply too large. This research project aims to design the chemical composition of a heterointerface for photovoltaic application with a targeted electronic property using a combination of electronic structure calculations and machine learning approach. More specially, the PI plans (a) to develop an in-depth understanding of the underlying physics that determines the electronic and atomic structure of the heterointerface using electronic structure calculations; and (b) to apply machine learning tools to explore hidden chemistry-property relationships of the interface, to predict the chemistry of the heterointerface for a desired electronic property. The proposed approach is a departure from the traditional time-consuming and expensive Edisonian trial-and-error approach of synthesis-testing experimental cycles; thus, it can substantially accelerate materials discovery. The PI anticipates that upon completion of this project a generic computational template to investigate structure-chemistry-property relationship of highly complex multi-component heterointerfaces will be generated. Finally, machine learning will be an integral part of future materials discovery. The knowledge gained from this project will help train student(s) on machine learning tools and their utility in materials research, increasing their exposure early in their careers to this growing and influential materials science field.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.