Automatic identification of slip pathways in ductile inorganic materials by combining the active learning strategy and NEB method

Ductile inorganic semiconductors have recently received considerable attention due to their metal-like mechanical properties and potential applications in flexible electronics. However, the accurate determination of slip pathways, crucial for understanding the deformation mechanism, still poses a great challenge owing to the complex crystal structures of these materials. In this study, we propose an automated workflow based on the interlayer slip potential energy surface to identify slip pathways in complex inorganic systems. Our computational approach consists of two key stages: first, an active learning strategy is utilized to efficiently and accurately model the interlayer slip potential energy surfaces; second, the climbing image nudged elastic band method is employed to identify minimum energy pathways, followed by comparative analysis to determine the final slip pathway. We discuss the validity of our selected feature vectors and models across various material systems and confirm that our approach demonstrates robust effectiveness in several case studies with both simple and complicated slip pathways. Our automated workflow opens a new avenue for the automatic identification of the slip pathways in inorganic materials, which holds promise for accelerating the high-throughput screening of ductile inorganic materials.