Representations of Materials for Machine Learning

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by a machine learning model. Datasets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and property of interests. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs of machine learning models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus, require further investigation.Comment: 20 pages, 5 figures, To Appear in Annual Review of Materials Research 5

Systematic Screening of DMOF-1 with NH2, NO2, Br and Azobenzene Functionalities for Elucidation of Carbon Dioxide and Nitrogen Separation Properties

In this study, dabco MOF-1 (DMOF-1) with four different functional groups (NH2, NO2, Br and azobenzene) has been successfully synthesized through systematic control of the synthesis condition of their parent framework. The functionalised DMOF-1 is characterized using various analytical techniques including PXRD, TGA and N2 sorption. The effect of the various functional groups on the performance of the MOFs for post-combustion CO2 capture is evaluated. DMOF-1s with polar functional groups are found to have better affinity with CO2 compared with the parent framework as indicated by higher CO2 heat of adsorption. However, imparting steric hindrance to the framework as in Azo-DMOF-1 enhances CO2/N2 selectivity, potentially as a result of lower N2 affinity for the framework