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Machine learning for Materials Science: 2D materials discovery and design

Grant number: 17/18139-6
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): January 01, 2018
Effective date (End): September 30, 2020
Field of knowledge:Physical Sciences and Mathematics - Physics
Principal Investigator:Adalberto Fazzio
Grantee:Gabriel Ravanhani Schleder
Home Institution: Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil

Abstract

Today, every field is being affected by a 'data deluge' resulting from advances in information technologies. Experimental, Theoretical, and Computational Sciences can all benefit from this progress and integrate it into a new fourth data-intensive science paradigm. The techniques needed to handle the large amounts of data produced only recently were made possible due to high-performance computation and new analytical developments. Innovations in advanced materials to technology are critical to society, determining its progress and being linked with the whole period (e.g. the Silicon Age). Materials innovations correspond to the majority of advancements in severals industries, however, their development traditionally demands a long and costly period, causing a lack of investment in this initial stage. Once a material is consolidated, it rarely is substituted because of the costs associated with large-scale production. Therefore, introducing material for specific sectors is increasingly important for their success, and recently several new technological niches need potential materials. We propose using a recently developed methodology to deal with large amounts of data produced by high-throughput computational simulations based on Density Functional Theory (DFT), in order to produce physically meaningful descriptors, that are functions describing the studied phenomena in terms of the material constituent atoms properties only. This methodology will be applied to understand and design novel two-dimensional materials focusing on electronic, thermal stability and photoelectrocatalytic properties, aiming at advanced cutting-edge technological applications. (AU)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SCHLEDER, GABRIEL R.; AZEVEDO, GUSTAVO M.; NOGUEIRA, ICAMIRA C.; REBELO, QUEREM H. F.; BETTINI, JEFFERSON; FAZZIO, ADALBERTO; LEITE, EDSON R. Decreasing Nanocrystal Structural Disorder by Ligand Exchange: An Experimental and Theoretical Analysis. Journal of Physical Chemistry Letters, v. 10, n. 7, p. 1471-1476, APR 4 2019. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.