|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Accelerated Genetic Algorithm via a Pre-trained Crystal Graph Convolutional Neural Network
||Jason Gibson, Richard Hennig
|On-Site Speaker (Planned)
Genetic algorithms (GA) are a key tool in crystal structure prediction which enables novel material discovery. However, the speed and scope of the GA is hindered by the computational cost of the density functional theory (DFT) calculations needed to evaluate the formation energy. In this study, we first acquired a dataset of approximately 130k relaxed crystal structures and formation energy calculations. The atoms within these structures were perturbed to create a training set of approximately 2 million crystal structures with the target value of each structure set to the structures relaxed formation energy. This training set was then used to pre-train a crystal graph convolutional neural network (CGCNN) to predict the relaxed formation energy of GA produced structures enabling a filtration process that removes high energy structures prior to DFT evaluation. As the GA progresses the CGCNN’s fidelity is increased by tuning the model on the DFT evaluated structures.
||Machine Learning, Computational Materials Science & Engineering,