Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model
Name
jam_89_12_121009.pdf
Description
Published version
Size
1.6 MB
Format
Adobe PDF
Checksum (MD5)
24d5f99f830ca44c451ce2f6199f4548
Author(s)
Buehler, Markus J
Date Issued
2022
Journal
Journal of Applied Mechanics
Publisher
ASME International
Citation
Buehler, Markus J. 2022. "Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model." Journal of Applied Mechanics, 89 (12).
Version
Final published version
Abstract
AbstractDynamic 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 distinct geometric details, including microstructures generated by a generative neural network used here to identify novel bio-inspired material designs for mechanical performance. For all cases, the model performs well and captures key aspects of material failure.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4055730
