Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model.

ABSTRACT: Dynamic fracture is an important area of materials analysis, assessing the atomic-level mechanisms by which materials fail over time. Here, we focus on brittle materials failure and show that an atomistically derived progressive transformer diffusion machine learning model can effectively describe the dynamics of fracture, capturing important aspects such as crack dynamics, instabilities, and initiation mechanisms. Trained on a small dataset of atomistic simulations, the model generalizes well and offers a rapid assessment of dynamic fracture mechanisms for complex geometries, expanding well beyond the original set of atomistic simulation results. Various validation cases, progressively more distinct from the data used for training, are presented and analyzed. The validation cases feature

SUBMITTER: Buehler MJ 

PROVIDER: S-EPMC9645704 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature