The Fully-Automated Nanoscale To Atomistic Structures from Theory and eXperiment (FANTASTX) code

The atomistic structure of complex nanoscale structures such as the grain boundaries or the nanoclusters is difficult to determine even using state-of-the-art experimental characterization techniques. So, the use of theoretical characterization methods is beneficial to address this problem. In this talk, we will discuss the modular FANTASTX (Fully Automated Nanoscale To Atomistic Structures from Theory and eXperiments) toolkit which explores the potential energy landscape for low energy structures that also match with experimental data. Experimental data types include scanning tunneling microscopy (STM), transmission electron microscopy (TEM), and x-ray pair distribution function (PDF), among others. Energetic information obtained from density functional theory or empirical potential calculations is used in conjunction with match to experimental data to guide the search. We will discuss the use of artificial intelligence/machine learning to accelerate the search. We will describe how FANTASTX is used to determine the atomistic structures of interfaces observed in STEM, nanoclusters observed with PDF, and surfaces observed with STM.