Dennis Trujillo gave a talk:
Applications of Deep Learning to Coherent Diffractive Imaging
Abstract. Convolutional Neural Networks have experienced a surge in interest over recent years as a viable technique in computer vision and other problems involving large datasets. Coherent diffractive imaging experiments typically generate extremely large datasets which require extensive data storage and extensive computation and manpower to screen through utilizing standard screening (expert analysis by eye) and phase retrieval algorithms. We apply these techniques in the domain of coherent diffractive imaging and phase retrieval for classification of objects of interest (topological phases) and in a regression scheme as an alternative to standard phase reconstruction algorithms.
Uche Anene presented a poster:
Ligand Functionalization for Enhanced Selective Gas Adsorption of Hydrostable STAM-17-OEt MOF
Abstract. Metal-organic framework (MOF), a class of porous crystalline materials, has received extensive attention due to their wide range of applications in areas such as gas storage and separation, catalysis and luminescence. However, its hydrolytic instability has been a major drawback and limit further implementation into new areas. Recently, STAM-17-OEt MOF was reported to overcome these shortcomings.1 Here we use density functional theory to investigate STAM-17-OEt for its selective sorption for technologically important gases such as CH4, CO, CO2, H2, N2 andH2O by using both molecular cluster and 3D periodic structural models. Further, MOF properties such as their sorption, selectivity, and their stability can be tuned through the functionalization of their organic linkers. In this work, we investigate howlinker functionalization modifies the electronic structure of STAM-17-OEt. Our findings will provide important insight on the unique binding characteristics of the different gas molecules and electronic structural factors associated with the functionalized STAM-17-OEt.
