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Advanced theoretical methods in the study of perovskites compounds and their alloys for solar cell applications

Grant number: 21/13913-0
Support Opportunities:Scholarships abroad - Research
Effective date (Start): October 01, 2022
Effective date (End): September 30, 2023
Field of knowledge:Physical Sciences and Mathematics - Physics - Condensed Matter Physics
Principal Investigator:Marcelo Marques
Grantee:Marcelo Marques
Host Investigator: Patrick Rinke
Host Institution: Divisão de Ciências Fundamentais (IEF). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil
Research place: Aalto University, Finland  


With the advent of global warming, the utilization of clean and renewable energy sources has become a prerequisite for the development of human society. Solar energy technologies appears with great prominence nowadays as one of the most important candidates to supply these demands. Particularly, there has been a revolution in this field in recent years with the use of materials called perovskite, providing devices with an efficiency comparable to standard Si technology, but with the great advantage of being much cheaper and easier to produce. However, the current challenge is to overcome the rapid degradation that these devices suffer from their continued use. It is desirable that this problem be solved while maintaining, or even increasing, the reached efficiency and also avoiding the toxicity associated with the presence of lead atoms. A good path to solve these challenges is the search for new kinds of perovskites compounds and the theoretical simulation appears as a fundamental step in this way. Nevertheless, if from an experimental point of view the perovskites are synthesized in a simpler way, the same does not happen with its theoretical description. These systems are much more complex than conventional semiconductors such as Si and GaAs, for several reasons as: the presence of different types of atoms, a very rich and complex internal geometry connected with polymorphism dependence of growth temperature, the presence of molecules in the called hybrid perovskites, the mandatory inclusion of the energy gap correction and spin-orbit effect resulting in a high computational cost, and the use of disordered perovskite alloys. In this context, my research group (Group of Semiconductors Materials and Nanotechnology-GMSN) from ITA has been theoretically researching perovskites materials in recent years. Despite the initial success obtained in this research, specially concerning perovskite alloys using a combination of a statistical model to treat the disorder (GQCA) and a method for quasiparticle correction (DFT-1/2), it is clearly desirable to include in our capabilities more sophisticated theoretical models to deal with all nuances that these complex materials demands, and this is the main point that this project concerns. The main goal is to go deeper in the theoretical study of perovskite compounds. In practice, this will be performed with the knowledge and application of two independent advanced theoretical methods: (i) Machine Leaning-ML model for alloys and (ii) the GW method for the rigorous calculation of excited states. The execution of this project will allow me, besides the immediate impact of the resultant publications, to acquire a new and advanced knowledge that, in addition with the models that we already developed as DFT-1/2 and GQCA, will improve the quality and possibilities not only for our perovskite research but also other topics that we are interested nowadays (2D materials and systems, oxides, etc). Within this framework is the justification of my option to work in Computational Electronic Structure Theory-CEST group of Aalto University-AU in Finland, led by Prof. Patrick Rinke. Specifically, CEST present: (a) beyond a solid research of ML models applied to materials in general, a specific development of ML applied to alloys, (b) GW method expertise, currently applying and developing this theoretical approach and (c) an active research on perovskite materials. Concluding, I expect as main results of this period in AU: (i) to produce relevant results publishing papers of perovskite research in journals with good scientific impact, (ii) learning and consequent mastery of Machine Learning models and GW method applied to materials, and (iii) to launch a productive and long time collaboration of GMSN and CEST group. Particularly, they are very interested in our expertise on efficient electronic structure method using DFT-1/2 method for large and complex materials. (AU)

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