Title: Combining Machine Learning Interatomic Potentials With X-Ray Absorption Spectroscopy to Resolve the Structure–Property Relationship in Functional Oxides
Research proposal No: 1.1.1.9/LZP/1/24/016
Duration: 01.03.2025.-29.02.2028.
Project Leader: Pjotrs Žguns
Total budget: EUR 185 510
ISSP UL budget: EUR 9275,50
Project description:
Advanced functional materials, such as photochromic and thermochromic oxides, have a wide range of functional capabilities and are of great interest for practical applications. However, understanding their structure–property relationships has been challenging and remains an open research quest. In this project, we propose to address this issue using a combination of locally sensitive X-ray Absorption Spectroscopy, specifically Extended X-ray Absorption Fine Structure (EXAFS), and Molecular Dynamics (MD) simulations based on Machine-Learning Interatomic Potentials (MLIPs). While EXAFS spectroscopy is a powerful experimental tool for probing the local atomic structure, it is plagued by ill analysis of spectra. To overcome this, we will apply cost-effective MD simulations based on MLIPs, which provide first-principles accuracy, to generate theoretical EXAFS spectra and validate them against experimental data for functional oxides. The results of this project will shed light on the atomistic structure of photochromic and thermochromic oxides, enabling further improvements in their performance.