Project: PN-IV-P1-PCE-2023-1785

Ttitle: Machine learning accelerated discovery of chalcogenides for memristors

Contractor: National Institute of Materials Physics (NIMP)

Project leader: Dr. Alin Velea

Project type: Exploratory research projects (PCE)

Start date: 08/01/2025

End date: 31/12/2027

Contracting Authority: UEFISCDI

Abstract:

GlassyMEM aims to accelerate the discovery of chalcogenide glasses for memristor applications through the use of machine learning methods. Chalcogenide phase change materials possess excellent electrical properties, including high resistivity contrast between glass and crystalline states and dynamic resistance tuning, making them ideal for memristors in AI, big data, and IoT applications. Traditional approaches for identifying new chalcogenide glasses rely on empirical rules, lacking a universal predictor of glass formation ability (GFA).

In this project, we will construct a comprehensive database integrating literature and experimental data obtained within this project. Chalcogenide libraries will be synthesized via magnetron co-sputtering, and their structural and compositional properties will be extensively characterized. Machine learning models will be developed and trained to predict GFA for chalcogenides, enabling faster and more efficient material screening.

Furthermore, the electrical properties of the most promising candidates will be investigated for memristor applications, aiming to achieve more powerful, energy-efficient, scalable, and cost-effective memristor devices. By adopting a data-driven approach, GlassyMEM will contribute to a deeper understanding of GFA, reducing costs, risks, and time associated with conventional methods. The project will propose viable candidates for advanced memristor technologies.