Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys

A descriptor-less machine learning (ML) model based only on charge density images extracted from density functional theory (DFT) is developed to predict stacking fault energies (SFE) in concentrated alloys. The model is based on convolutional neural networks (CNNs) as one of the promising ML techniques for dealing with complex images and data. Identification of correct descriptors is a key bottleneck to develop ML models for predicting materials properties. Often, in most ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used as descriptors which have limitations because these properties change in concentrated alloys when multiple elements are mixed to form a solid solution. We illustrate that, within the scope of DFT, the search for descriptors can be circumvented by electronic charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. The performance of our model is demonstrated by predicting SFE of concentrated alloys with an RMSE and R 2 of 6.18 mJ/m2 and 0.87, respectively, validating the accuracy of the proposed approach.

Automated summary

We developed a descriptor-less machine learning model based on charge density images extracted from DFT to predict stacking fault energies in concentrated alloys. The model utilizes convolutional neural networks as a promising technique for complex images and data, and avoids the need for traditional physical descriptors by utilizing electronic charge density. The model demonstrates high accuracy in predicting the stacking fault energies.