Speaker
Description
Extended X-ray Absorption Fine Structure (EXAFS) spectroscopy is a powerful technique for probing local atomic structure and thermal disorder. Its high sensitivity to atomic vibrations results in pronounced temperature-dependent damping of the EXAFS signal, making the technique a promising tool for non-contact thermometry, with applications ranging from nanoparticles [1] to materials under extreme conditions [2]. Absolute temperature determination from measured EXAFS spectra, however, typically requires either laborious calibration [1] or the use of analytical models, such as the Debye model, which is limited to monoatomic solids [3]. Computational approaches based on Molecular Dynamics (MD) reference standards [2] are, to date, limited in precision owing to the prohibitive cost of ab initio methods for modelling thermal disorder.
Here, we present a quantitative and computationally efficient framework to determine sample temperature based on MD simulations with Universal Machine Learning Interatomic Potentials (uMLIPs) [4] in combination with an ab initio multiple-scattering EXAFS formalism [5]. Pre-trained uMLIPs are fine-tuned on compound-specific Density Functional Theory (DFT) data, enabling accurate calculation of forces and temperature-dependent atomic vibrations at minimal computational cost [6]. Configuration-averaged EXAFS spectra are then computed at various points across a wide temperature range based on the results of MD simulations. Finally, experimental EXAFS spectra are fitted to these computational references to determine the corresponding sample temperature on an absolute temperature scale.
We validate this approach on NiO and ZnO, achieving accurate temperature determination from room temperature up to 1000 K. This framework provides a robust and computationally efficient route for quantitative EXAFS thermometry, applicable to a broad range of materials.
References
[1] Van de Broek B. et al. (2011). Temperature determination of resonantly excited plasmonic branched gold nanoparticles by X-ray absorption spectroscopy. Small, 7, 2498-2506.
[2] Sio H. et al. (2023). Extended X-ray absorption fine structure of dynamically-compressed copper up to 1 terapascal. Nature Communications, 14, 7046.
[3] Kuzmin A. et al. (2024). The Use of the correlated Debye model for extended x-ray absorption fine structure-based thermometry in body-centered cubic and face-centered cubic metals. Physica Status Solidi A, 222, 2400623.
[4] Deng B. et al. (2023). CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling. Nature Machine Intelligence, 5, 1031-1041.
[5] Rehr J.J. & Albers R.C. (2000). Theoretical Approaches to X-Ray Absorption Fine Structure. Rev. Mod. Phys. 72, 621-654.
[6] Žguns P. et al. (2025). Benchmarking CHGNet universal machine learning interatomic potential against DFT and EXAFS: The case of layered WS2 and MoS2. Journal of Chemical Theory and Computation, 21, 8142-8150.
