| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Computational Discovery, Understanding, and Design of Multi-principal Element Materials
|
| Presentation Title |
Charge-Density based Convolutional Neural Networks for Property Prediction in High Entropy Alloys |
| Author(s) |
Jacob Fisher, Serveh Kamrava, Pejman Tahmasebi, Dilpuneet S. Aidhy |
| On-Site Speaker (Planned) |
Dilpuneet S. Aidhy |
| Abstract Scope |
A descriptor-less machine learning (ML) model based only on charge density extracted from density functional theory (DFT) is developed to predict properties in concentrated alloys. Often, in most ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used 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 charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. 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 stacking fault energy (SFE) of concentrated alloys with an RMSE and R2 of 6.18 mJ/m2 and 0.87, respectively, validating the accuracy of the proposed approach. |