Christina Tan

Chemical and Biomolecular Engineering

Faculty Advisor: Yamil J Colón

Automated Detection of Defects in Porous Materials with Machine Learning

It is understood that the presence of defects can be a determining factor for a multitude of energy-related applications including catalysis, gas storage, separations, etc. Determining the presence and quantity of defects in a material can be a challenging endeavor requiring advanced characterization techniques. This project seeks to identify and quantify defects in porous materials with standard characterization techniques like a simple adsorption isotherm using machine learning algorithms. The Colón group trains anomaly detection algorithms to determine if a material has a defect and how many are present. The group is currently testing algorithms on the data set of Zr-based structures with known quantities of defects and will be moving on to finalizing a paper to be published. By the end of this project, the algorithms will be tested on data from molecular simulations and experimental systems. It is expected that the results will be of great value to the porous materials and energy communities.

Christina Tan Final Report