|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||Charge-density Based Convolutional Neural Networks for Stacking Fault Energy Prediction in Concentrated Alloys
||Jacob Fischer, Gaurav Arora, Serveh Kamrava, Pejman Tahmasebi, Dilpuneet S. Aidhy
|On-Site Speaker (Planned)
||Dilpuneet S. Aidhy
A descriptor-less machine learning (ML) approach based only on charge density extracted from density functional theory (DFT) is developed to predict stacking fault energies (SFE) in concentrated alloys. Often, in ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used which have limitations because these properties ‘adjust’ in concentrated alloys due to varying nearest neighbor environments. We illustrate that, within the scope of DFT, the search for descriptors can be circumvented by charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. The descriptors are captured by charge-density inherently. The model is based on convolutional neural networks (CNNs) as one of the promising ML techniques. The performance of our model is evaluated by predicting SFE of concentrated alloys with an RMSE and R2 of 6.18 mJ/m2 and 0.87, respectively, validating the accuracy of the approach.
||Planned: Metallurgical and Materials Transactions