Executive Summary : | The need for clean and sustainable energy for a carbon neutral economy is driving the research to find efficient catalysts. In this context, heterogeneous photo-catalytic materials are employed widely in water treatment, hydrogen evolution reaction, and carbon capture and this has led to significant effort in the search for suitable catalysts for such processes. However, the limiting factors of this technology to develop beyond laboratory conditions are charge recombination, low quantum efficiency, chemical instability, and poor economy of production/operation. In this scenario, the focus is on designing appropriate catalysts, by tuning the morphology. Current proposal addresses the development and application of a promising class of two dimensional (2D) materials called Janus materials that are anisotropic and bi-phasic and possess higher photo conversion efficiencies. We will explore various possibilities of tuning the properties of Janus materials by altering their morphology, doping and defect induction to enhance the catalytic performance, electric near-field and efficient charge separation mechanisms. Machine learning (ML) algorithms and density functional theory (DFT) calculations will be used to predict the photo electric conversion coefficient of the Janus layers and validated by experiments. Therefore, the main goals of the proposal is to discover 2D Janus layers and hetero structures with higher photo conversion efficiencies. The objectives to achieve this goal is (a) first principles DFT based modelling of 2D Janus materials that has stability and enhanced photo- conversion efficiencies (b) ML predictions using the outputs from the DFT calculations (c) experimental synthesis and characterization to verify the DFT and ML findings (d) validation and correction using the outputs of experiments. The Janus 2D materials discovered in this manner will be added to existing repositories such as NOMAD, and github and made available in the public domain. |