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, 2021
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter 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

(10)

(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)

GIUSTINO, FELICIANO; LEE, JIN HONG; TRIER, FELIX; BIBES, MANUEL; WINTER, STEPHEN M.; VALENTI, ROSER; SON, YOUNG-WOO; TAILLEFER, LOUIS; HEIL, CHRISTOPH; FIGUEROA, I, ADRIANA; PLACAIS, BERNARD; WU, QUANSHENG; YAZYEV, V, OLEG; BAKKERS, ERIK P. A. M.; NYGARD, JESPER; FORN-DIAZ, POL; DE FRANCESCHI, SILVANO; MCIVER, J. W.; TORRES, L. E. F. FOA; LOW, TONY; KUMAR, ANSHUMAN; GALCERAN, REGINA; VALENZUELA, SERGIO O.; COSTACHE, V, MARIUS; MANCHON, AURELIEN; KIM, EUN-AH; SCHLEDER, GABRIEL R.; FAZZIO, ADALBERTO; ROCHE, STEPHAN. The 2021 quantum materials roadmap. JOURNAL OF PHYSICS-MATERIALS, v. 3, n. 4 OCT 2021. Web of Science Citations: 1.

GIORDANO, GABRIELA F.; VIEIRA, LUIS C. S.; GOMES, ALEXANDRE O.; DE CARVALHO, ROGERIO M.; KUBOTA, LAURO T.; FAZZIO, ADALBERTO; SCHLEDER, GABRIEL R.; GOBBI, ANGELO L.; LIMA, RENATO S. Distilling small volumes of crude oil. FUEL, v. 285, FEB 1 2021. Web of Science Citations: 0.

PEZO, ARMANDO; FOCASSIO, BRUNO; SCHLEDER, GABRIEL R.; COSTA, MARCIO; LEWENKOPF, CAIO; FAZZIO, ADALBERTO. Disorder effects of vacancies on the electronic transport properties of realistic topological insulator nanoribbons: The case of bismuthene. PHYSICAL REVIEW MATERIALS, v. 5, n. 1 JAN 19 2021. Web of Science Citations: 0.

FOCASSIO, BRUNO; SCHLEDER, GABRIEL R.; PEZO, ARMANDO; COSTA, MARCIO; FAZZIO, ADALBERTO. Dual topological insulator device with disorder robustness. Physical Review B, v. 102, n. 4 JUL 14 2020. Web of Science Citations: 0.

SCHLEDER, GABRIEL R.; ACOSTA, CARLOS MERA; FAZZIO, ADALBERTO. Exploring Two-Dimensional Materials Thermodynamic Stability via Machine Learning. ACS APPLIED MATERIALS & INTERFACES, v. 12, n. 18, p. 20149-20157, MAY 6 2020. Web of Science Citations: 2.

SOUZA JUNIOR, JOAO BATISTA; SCHLEDER, GABRIEL RAVANHANI; COLOMBARI, FELIPPE MARIANO; DE FARIAS, MARCELO ALEXANDRE; BETTINI, JEFFERSON; VAN HEEL, MARIN; PORTUGAL, RODRIGO VILLARES; FAZZIO, ADALBERTO; LEITE, EDSON ROBERTO. Pair Distribution Function from Electron Diffraction in Cryogenic Electron Microscopy: Revealing Glassy Water Structure. Journal of Physical Chemistry Letters, v. 11, n. 4, p. 1564-1569, FEB 20 2020. Web of Science Citations: 0.

DA SILVA, GIULIA S.; DE OLIVEIRA, LUIZA P.; COSTA, GABRIEL F.; GIORDANO, GABRIELA F.; NICOLICHE, CAROLINE Y. N.; DA SILVA, ALEXANDRE A.; KHAN, LATIF U.; DA SILVA, GABRIELA H.; GOBBI, ANGELO L.; SILVEIRA, JOSE V.; FILHO, ANTONIO G. SOUZA; SCHLEDER, GABRIEL R.; FAZZIO, ADALBERTO; MARTINEZ, DIEGO S. T.; LIMA, RENATO S. Ordinary microfluidic electrodes combined with bulk nanoprobe produce multidimensional electric double-layer capacitances towards metal ion recognition. SENSORS AND ACTUATORS B-CHEMICAL, v. 305, FEB 15 2020. Web of Science Citations: 1.

SCHLEDER, GABRIEL RAVANHANI; PADILHA, ANTONIO CLAUDIO M.; ROCHA, ALEXANDRE REILY; DALPIAN, GUSTAVO MARTINI; FAZZIO, ADALBERTO. Ab lnitio Simulations and Materials Chemistry in the Age of Big Data. JOURNAL OF CHEMICAL INFORMATION AND MODELING, v. 60, n. 2, p. 452-459, FEB 2020. Web of Science Citations: 1.

COSTA, MARCIO; SCHLEDER, GABRIEL R.; NARDELLI, MARCO BUONGIORNO; LEWENKOPF, CAIO; FAZZIO, ADALBERTO. Toward Realistic Amorphous Topological Insulators. Nano Letters, v. 19, n. 12, p. 8941-8946, DEC 2019. Web of Science Citations: 1.

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: 1.