There are several ongoing themes present in the Fons research group some of which are listed below. The basic theme is to understand atomic level structure and use the resulting understanding to optimize functionality. This includes the use of synchrotron radiation to investigate the physical and electronic structure of solids using tools such as x-ray absorption spectroscopy (XAFS) or high-energy photoemission spectroscopy. XAFS is a technique particular useful for understanding the structure of amorphous solids. Ultrafast dynamics has also been a topic of interest with collaborative efforts including using the free-electron laser SACLA at SPring-8, coherent phonon dynamics, or ab-initio molecular dynamics calculations. In addition to the use of ab-initio based computational techniques in recent years, the Fons group has used machine learning techniques both to predict good candidate materials for experiments such as selector materials as well as for the analysis of large datasets of molecular dynamic data. The use of machine learning techniques allows not only large improvements in the speed of analyzing large ab-initio computational datasets, but also large increases in the speed and size of molecular dynamics calculations by means of the use of machine-learned force fields based upon ab-initio calculations.

Machine Learning-based Analysis of ab-initio MD

Using machine learning to analyze crystallization dynamics

Machine Learned Force Fields

Using machine learned force fields to speed-up molecular dynamics runs

Density-Functional Theory

ab-initio calculations of material properties, structure, and dynamics.

Ultrafast Dynamics

Using lasers and x-rays to probe ultrafast dynamics.

Phase-Change Materials

Materials whose structure is used for storage.

Structure of Amorphous Materials

Using synchrotron radiation to probe amorphous structure.