Machine learning for two-dimensional materials' properties

• Grant number: 21/12204-6
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: February 04, 2022
• End date: February 03, 2023
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Adalberto Fazzio
• Grantee: Bruno Focassio
• Supervisor: Stefano Sanvito
• Host Institution: Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Institution abroad: Trinity College Dublin, Ireland
• Associated to the scholarship: 19/04527-0 - Interface between crystalline topological insulators and 2D-trivial materials: defect proximity study, BP.DD

Abstract

Materials are represented by their atoms, composition, and structure (ACS). Materials with different ACS descriptions present a different set of properties. However, there is a complex and intriguing relationship between the ACS and materials' properties. Machine learning can explore such relations and accelerate the evaluation of properties by using fingerprints of known materials to find an approximate function to predict the property of unknown materials. Topological insulators emerge as exotic systems featuring insulating bulk and symmetry-protected metallic boundary states with potential applications in spintronic and spin-orbitronic devices, low energy loss devices, and quantum computing. In real materials, the topological properties are sensible to structural modifications such as defects, temperature, chemical substitution, strain, and amorphous phases. These modifications are shown to transform topological materials into trivial ones but also turn trivial materials into topological ones. We propose to study the relationship between materials' structure and topological properties using machine learning and data-driven methods. We will investigate both global and local structure descriptors to gain insight into the role of structure and symmetries. Also, we will explore the local effects on global properties. Finally, this could allow us to apply our models to study disordered materials and realistic structures. (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)

FOCASSIO, BRUNO; DOMINA, MICHELANGELO; PATIL, URVESH; FAZZIO, ADALBERTO; SANVITO, STEFANO. Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations. NPJ COMPUTATIONAL MATERIALS, v. 9, n. 1, p. 10-pg., 2023-05-29. (19/04527-0, 17/02317-2, 21/12204-6)