Pattern recognition protocol applied to artificial spins systems

• Grant number: 23/00137-8
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: March 01, 2023
• End date: August 27, 2023
• Field of knowledge: Physical Sciences and Mathematics - Physics - Condensed Matter Physics
• Principal Investigator: Kleber Roberto Pirota
• Grantee: Nathan Dias Canabrava Cruz
• Host Institution: Instituto de Física Gleb Wataghin (IFGW). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 17/10581-1 - Emergent phenomena in reduced dimension systems, AP.ESP

Abstract

Artificial Spin Ices (GSI) are nanomagnet networks, mostly arranged in two-dimensional patterns, which have been successfully used as experimental platforms in the investigation of cooperative and exotic phenomena in models of frustrated spin networks. The rich magnetic phase diagram presented by Ising-type nanomagnets arranged at the vertices of a two-dimensional Kagomé lattice (AKSI) has four distinct phases: high-temperature paramagnetic, two different spin liquid phases (Spin ice 1/SI1 and Spin ice 2/SI2) and an ordered ground state. From a theoretical point of view, Hamiltonians that take into account short- or long-range interactions are simulated via Monte Carlo and the microstate obtained is analyzed via calculations of correlations between neighbors of various levels, from which the various thermodynamic quantities such as specific heat can be obtained. , entropy, magnetization etc. Experimentally, the nanomagnet network is analyzed via some magnetic imaging technique (Magnetic Force Microscopy/MFM or Photo Emission Electron Microscopy/PEEM) and the spin correlations obtained (in direct or reciprocal space) are compared to those obtained by theoretical models appropriate. This project proposes the use of pattern recognition (machine learning) to identify the different phases via initial magnetization curves, something much simpler to obtain in conventional magnetometers present in many laboratories. We will also use pattern recognition to explore the rich physical phenomenology presented by such systems, since learning protocols can help decode the physical processes involved in the dynamics of magnetization of KASI as a function of effective temperature.

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)

CECCHI, BRENO MALVEZZI; CRUZ, NATHAN; KNOBEL, MARCELO; PIROTA, KLEBER ROBERTO. Magnetic phase recognition of artificial kagome spin ice through initial magnetization curve. PHYSICAL REVIEW B, v. 108, n. 1, p. 7-pg., 2023-07-05. (17/10581-1, 23/00137-8, 19/23317-6)