Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
Search Results
Item Review - A Review of 2D Perovskites and Carbon-Based Nanomaterials for Applications in Solar Cells and Photodetectors(IOP Publishing Ltd, 2021) Omprakash, P.; Viswesh, P.; Bhat Panemangalore, D.B.Photonic devices such as solar cells and photodetectors that produce electricity play a vital role in our daily life for applications such as fibre optic communication systems, process control, and also in defence related applications. Today, two-dimensional perovskites that belong to the class of emerging materials show promising energy applications. 2D perovskites have been investigated for their exceptional properties such as high optical absorption coefficients, structural diversity and tuneable bandgaps which allow their application as active light absorbing materials to develop solar cells and photodetectors. Carbon-based nanomaterials have also found applications as transparent electrodes, charge acceptors and photosensitive layers in solar cells and photodetectors due to properties such as excellent electrical conductivity, high optical transparency, high surface area and remarkable mechanical strength. There has been growing interest in research on devices using these materials to improve their feasibility, ease of production and performance. With the growing urgency of switching to alternate sources of energy and increasing demands for highly accurate and fast sensors, the development and application of such novel materials are essential. Hence, the current state of understanding of these materials and their applications in the field of solar cells and photodetectors are summarized in this review article. © 2021 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.Item Graph representational learning for bandgap prediction in varied perovskite crystals(Elsevier B.V., 2021) Omprakash, P.; Manikandan, B.; Sandeep, A.; Shrivastava, R.; Viswesh, P.; Bhat Panemangalore, D.B.Perovskites are an important class of materials that are actively researched for applications in solar cells and other optoelectronic devices due to their ease of fabrication and tuneable bandgaps. High throughput computational techniques like Density Functional Theory (DFT) and Machine Learning (ML) are viable methods to accelerate discovery of new perovskite materials with favourable properties. ML specifically is faster and requires lesser computational power. We recognized the importance of having robust datasets for ML and hence collated a dataset of varied perovskite structures along with their indirect bandgaps. We employed a graph representational learning technique and trained a model that predicted bandgaps for all types of perovskites. The model has a mean absolute error of 0.28 eV and can predict bandgap in a few milliseconds. The metric of generalization gap is introduced to quantify the performance of ML models. This metric will help in building more generalized models that can predict properties for novel materials. Furthermore, we believe that these computational techniques should be user-friendly to those less experienced in the field. Hence, for researchers unacquainted with DFT or ML, we built a pipeline that abstracts the specific processes. This makes it easier for material scientists to quickly screen viable inorganic perovskite compounds allowing them to synthesize and experiment on the more promising compounds. © 2021 Elsevier B.V.