A density functional theory parameterized neural network model of zirconia


chenw33 - Posted on 28 February 2017

Project Description: 

We have developed a Behler-Parrinello Neural Network (BPNN) that can be employed to calculate energies and forces of zirconia bulk structures with oxygen vacancies with similar accuracy as that of the density functional theory (DFT) calculations that were used to train the BPNN. In this work, we have trained the BPNN potential with a reference set of 2178 DFT calculations and validated it against a dataset of untrained data. We have shown that the bulk structural parameters, equation of states, oxygen vacancy formation energies and diffusion barriers predicted by the BPNN potential are in good agreement with the reference DFT data. The transferability of the BPNN potential has also been benchmarked with the prediction of structures that were not included in the reference set. The evaluation time of the BPNN is orders of magnitude less than corresponding DFT calculations, although the training process of the BPNN potential requires non-negligible amount of computational resources to prepare the dataset. The computational efficiency of the NN enabled it to be used in molecular dynamics simulations of the temperature dependent diffusion of an oxygen vacancy, and the corresponding diffusion activation energy.

Research Project Details
Project Duration: 
02/2017 to 02/2018
Project Significance: 
The properly trained BPNN potential is applicable to a wide range of systems with an arbitrary accuracy. The evaluation time of the BPNN potential is several orders of magnitude faster than DFT calculation and have a nearly linear scaling behavior with respect to system size which makes them more suitable for large-scale MD and Monte Carlo simulations. Also, such mathematical potential also provides a promising approach for accurately investigating the properties of large TM oxides nanoalloys composed of thousands of atoms.
Results Achieved: 
NA
Remarks: 
High efficient computing resources are required for DFT calculation and Artificial Neural Network training.