A Deep Learning Model for Atomic Structures Prediction Using X-ray Absorption Spectroscopic Data

Understand

A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES).

• The output layer of the DNN can be adjusted to form a classifier or regressor, to predict the local and overall coordination environments, respectively.
• Using Li3FeO3.5 as a model system, it was demonstrated that the prediction accuracy of the DNN classifier is higher than 98%, and the predictions of the DNN regressor also showed notable agreement with the ground truth.
• Therefore, despite its simplicity, this DNN architecture can be expected to be generally capable of predicting the structural properties of various systems.