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

点击翻译按钮获取中文摘要

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.