Data-driven design of energy materials utilizing transmission electron microscopy and density functional theory data

Abstract

Understanding the structure-property-process relationship is crucial for designing and optimizing materials for various material fields. In the field of materials science and engineering, there have been paradigm shifts from empirical science to theoretical science, computational science, and now data-driven science. The data-driven approach utilizes big data and machine learning to extract structural and property features from research data, accelerating materials discovery. To facilitate data-driven materials design, the process involves acquiring, managing, analyzing, and applying research data. The research data such as imaging and modeling are used to extract structural and property features by machine learning algorithms to establish correlations between them. This enables inverse design, where desired structures and properties are derived from optimal property points, expediting materials development. In the context of energy materials research, advanced battery technologies require a deep understanding of the structure-property relationship. The complex systems of materials like NCM and NVPF necessitate novel research methodologies. This work integrates data-driven materials design into energy materials research, focusing on electrode materials for lithium-ion batteries and sodium-ion batteries. The workflow includes TEM analysis using CNN models, density functional theory calculations, and the establishment of structure-property relationships. By leveraging machine learning and inverse design, new materials with improved performance can be designed more efficiently. This research is a milestone in accelerating materials discovery across various fields, offering a blueprint for materials design based on the relationship between TEM and DFT data, structure, and property. The data-driven approach has the potential to revolutionize materials development and open up new possibilities for designing advanced materials with tailored properties.