Machine learning methods applied to the study of interfaces between semiconductors

• Grant number: 18/11641-0
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: July 01, 2018
• End date: June 30, 2023
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Gustavo Martini Dalpian
• Grantee: Elton Ogoshi de Melo
• Host Institution: Centro de Ciências Naturais e Humanas (CCNH). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Associated research grant: 17/02317-2 - Interfaces in materials: electronic, magnetic, structural and transport properties, AP.TEM

Abstract

This project aims to develop theoretical/computational studies on interfaces between different semiconductor materials. This type of interface is of fundamental importance for the development of a number of devices, including transistors and solar cells. We will use theoretical methods based on density functional theory to understand the main properties of a series of interfaces, starting our study through the interfaces between hybrid perovskites and oxide materials. We will use the generated databases to train machine learning algorithms to be able to predict the properties of a series of other interfaces. (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)

ACOSTA, CARLOS MERA; OGOSHI, ELTON; FAZZIO, ADALBERTO; DALPIAN, GUSTAVO M.; ZUNGER, ALEX. The Rashba Scale: Emergence of Band Anti-crossing as a Design Principle for Materials with Large Rashba Coefficient. MATTER, v. 3, n. 1, p. 145-165, JUL 1 2020. (17/02317-2, 18/11641-0, 18/11856-7)

NASCIMENTO, GABRIEL M.; OGOSHI, ELTON; FAZZIO, ADALBERTO; ACOSTA, CARLOS MERA; DALPIAN, GUSTAVO M.. High-throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds. SCIENTIFIC DATA, v. 9, n. 1, p. 18-pg., 2022-04-29. (17/02317-2, 19/04176-2, 18/11856-7, 18/11641-0)

ACOSTA, CARLOS MERA; OGOSHI, ELTON; SOUZA, JOSE ANTONIO; DALPIAN, GUSTAVO M.. Machine Learning Study of the Magnetic Ordering in 2D Materials. ACS APPLIED MATERIALS & INTERFACES, v. 14, n. 7, p. 15-pg., 2022-02-08. (18/11856-7, 18/11641-0, 17/02317-2)