Measurements of Surface Diffusivity and Coarsening During Pulsed Laser Deposition

Pulsed Laser Deposition (PLD) of homoepitaxial SrTiO3 was studied with in-situ x-ray specular reflectivity and surface diffuse x-ray scattering. Unlike prior reflectivity-based studies, these measurements access both the time- and the length-scales of the evolution of the surface morphology during growth. In particular, we show that this technique allows direct measurements of the diffusivity for both inter- and intra-layer transport. Our results explicitly limit the possible role of island break-up, demonstrate the key roles played by nucleation and coarsening in PLD, and place an upper bound on the Ehrlich-Schwoebel (ES) barrier for downhill diffusion

Multiple Time Scales in Diffraction Measurements of Diffusive Surface Relaxation

We grew SrTiO3 on SrTiO3 (001) by pulsed laser deposition, using x-ray scattering to monitor the growth in real time. The time-resolved small angle scattering exhibits a well-defined length scale associated with the spacing between unit cell high surface features. This length scale imposes a discrete spectrum of Fourier components and rate constants upon the diffusion equation solution, evident in multiple exponential relaxation of the "anti-Bragg" diffracted intensity. An Arrhenius analysis of measured rate constants confirms that they originate from a single activation energy.Comment: 4 pages, 3 figure

A Social Network-Guided Approach to Machine Learning for Metal-Organic Framework Property Prediction

The number of new materials and applications of these materials is experiencing rapid growth. ‎Today, increased computational power and the established use of automated machine learning ‎approaches make data science tools available, which provide an overview of the chemical space, ‎support the choice of appropriate materials, and predict specific properties of materials for the ‎desired application. Among the different data science tools, graph theory approaches, where data ‎generated from numerous real-world applications are represented as a graph (network) of ‎connected objects, has been widely used in a variety of scientific fields such as social sciences, ‎health informatics, biological sciences, agricultural sciences, and economics. In this work, we ‎describe applying a particular graph theory approach, social network analysis (SNA), to the metal-organic framework (MOF). To demonstrate MOF materials, we construct a social network called ‎MOFSocialNet from geometrical MOFs descriptors in the CoRE-MOFs database. The MOFSocialNet ‎is an undirected, weighted, and heterogeneous social network; following the construction of this ‎graph, a set of social network analysis processes is conducted to extract valuable knowledge from ‎the MOFs data using graph machine learning algorithms. Community detection is one of the well-known SNA techniques employed on the MOFSocialNet to extract the most similar MOF ‎communities. To evaluate whether the properties of new MOFs can be predicted using MOF ‎communities, we randomly chose three from the CoRE MOFs database. For these MOFs, we ‎excluded the crystal density as input during featurization and placed the MOFs within the ‎MOFSocialNet. The crystal density of the new MOFs is predicted by simply averaging the crystal ‎density of the ten nearest neighbors. ‎ Additionally, communities extracted from MOFSocialNet can be leveraged to predict MOF gas ‎adsorption properties for CO2 and CH4.