Leveraging machine learning to determine nanoscale structures from theory and experiments*

Determining the atomistic details of nanoscale structures is a fundamental problem. Although there are both experimental and computational methods to determine these nanoscale structures, they both possess limitations. We develop the FANTASTX code (Fully Automated Nanoscale To Atomistic Structure from Theory and eXperiment) to overcome the limitations of either by combining both experimental and computational data using machine learning techniques. We demonstrate the effectiveness of FANTASTX by determining the structures of nanoparticles and solid interfaces from x-ray and electron microscopy data combined with atomistic and first principles energies, using multi-objective optimization and a variety of canonical and grand canonical sampling algorithms.