| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
|
| Presentation Title |
Predicting Electronic Density of States of Nanoparticles by Principal Component Analysis and Crystal Graph Convolutional Neural Network |
| Author(s) |
Kihoon Bang, Byung Chul Yeo, Doosun Hong, Donghun Kim, Sang Soo Han, Hyuck Mo Lee |
| On-Site Speaker (Planned) |
Hyuck Mo Lee |
| Abstract Scope |
A computational cost in calculating DOS by density functional theory (DFT) is quite high, in particular, for nanoparticles (NPs) with large number of atoms. Herein, we propose a machine-learning method to fastly predict DOS patterns of metallic NPs by combining a principal component analysis (PCA) and a crystal graph convolutional neural network (CGCNN). Within the PCA-CGCNN framework, we can predict DOSs of Pt, Pd, and Au NPs with a reasonable accuracy compared to DFT calculation, where effects of various sizes and shapes of metallic NPs have also been explored. In addition, the PCA-CGCNN method can be applied into various bimetallic alloy structures such as core-shell type, homogeneously mixed ones, and phase separated ones. We also developed a separate-learning technique. Moreover, our PCA-CGCNN method shows ~8,000 times faster than the DFT method. This clearly reveals that our PCA-CGCNN method provides a new paradigm in the field of an electronic structure calculation. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |