Universal and Experiment-calibrated Prediction of XANES through Crystal Graph Neural Network and Transfer Learning Strategy

TL;DR

This paper introduces a universal crystal graph neural network trained on simulated data for XANES prediction, further calibrated with experimental data via transfer learning, achieving high accuracy and broad element coverage.

Contribution

It develops a universal GNN model trained on simulated data for XANES prediction across 48 elements, and employs transfer learning for experimental calibration, improving accuracy and applicability.

Findings

Achieved low average relative square error of 0.020223 in XANES prediction.

Reduced edge energy misalignment errors by about 80% after transfer learning.

Demonstrated universal prediction capability across multiple elements.

Abstract

Theoretical simulation is helpful for accurate interpretation of experimental X-ray absorption near-edge structure (XANES) spectra that contain rich atomic and electronic structure information of materials. However, current simulation methods are usually too complex to give the needed accuracy and timeliness when a large amount of data need to be analyzed, such as for in-situ characterization of battery materials. To address these problems, artificial intelligence (AI) models have been developed for XANES prediction. However, instead of using experimental XANES data, the existing models are trained using simulated data, resulting in significant discrepancies between the predicted and experimental spectra. Also, the universality across different elements has not been well studied for such models. In this work, we firstly establish a crystal graph neural network, pre-trained on simulated XANES data covering 48 elements, to achieve universal XANES prediction with a low average relative square error of 0.020223; and then utilize transfer learning to calibrate the model using a small experimental XANES dataset. After calibration, the edge energy misalignment error of the predicted S, Ti and Fe K edge XANES is significantly reduced by about 80%. The method demonstrated in this work opens up a new way to achieve fast, universal, and experiment-calibrated XANES prediction.