Fig. 1: Strategy and workflow of the construction of NNP for dislocation plasticity in ceramics in this work.
From: Neural network potential for dislocation plasticity in ceramics
The dislocation-related structures were generated using our open-source ADAIS code45 together with the VASP code46, enabling high-throughput DFT calculations. The basic structures provide a description of the essential properties of materials, which serve as the foundation of an NNP. For more details on the implementations, workflows, and execution examples of the ADAIS code, one may refer to our previous publication45. In addition to the broad validation across various properties, the effectiveness of the training dataset and NNPs was further confirmed by assessing the similarity to the training dataset and evaluating the uncertainty through the ensemble-based method, using snapshots derived from large-scale simulations.
