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The atomistic structure determines the stability and properties of a material and its potential applications. We develop the software tools Ingrained and FANTASTX (Fully Automated Nanoscale To Atomistic Structure from Theory and eXperiments) to determine atomistic structures from experimental data with the help of theory and machine learning. The ingrained software [1] constructs a grain boundary structure or validates a surface structure based on experimental STEM or STM images, respectively. We will show examples of grain boundary structures created using Ingrained which are used as starting points for further analysis. This provides a path to understand complex mechanisms from characterization data. We demonstrate the utility of Ingrained which was used to successfully determine the surface structure of hydrogenated borophene [2]. FANTASTX is a multi-objective genetic algorithm tool that helps to find …