New method to predict stress at atomic scale

Date:
November 9, 2021
Source:
University of Illinois Grainger College of Engineering
Summary:
The amount of stress a material can withstand before it cracks
is critical information when designing aircraft, spacecraft, and
other structures. Aerospace engineers used machine learning for
the first time to predict stress in copper at the atomic scale.



FULL STORY ==========================================================================
The amount of stress a material can withstand before it cracks is critical information when designing aircraft, spacecraft, and other structures.

Aerospace engineers at the University of Illinois Urbana-Champaign used
machine learning for the first time to predict stress in copper at the
atomic scale.


========================================================================== According to Huck Beng Chew and his doctoral student Yue Cui, materials,
such as copper, are very different at these very small scales.

"Metals are typically polycrystalline in that they contain many grains,"
Chew said. "Each grain is a single crystal structure where all the atoms
are arranged neatly and very orderly. But the atomic structure of the
boundary where these grains meet can be very complex and tend to have
very high stresses." These grain boundary stresses are responsible
for the fracture and fatigue properties of the metal, but until now,
such detailed atomic-scale stress measurements were confined to molecular dynamics simulation models. Using data- driven approaches based on machine learning enables the study to quantify, for the first time, the grain
boundary stresses in actual metal specimens imaged by electron microscopy.

"We used molecular dynamics simulations of copper grain boundaries
to train our machine learning algorithm to recognize the arrangements
of the atoms along the boundaries and identify patterns in the stress distributions within different grain boundary structures," Cui said.

Eventually, the algorithm was able to predict very accurately the grain boundary stresses from both simulation and experimental image data with
atomic- level resolution.



==========================================================================
"We tested the accuracy of the machine learning algorithm with lots of different grain boundary structures until we were confident that the
approach was reliable," Cui said.

Cui said that the task was more challenging than they imagined, and they
had to include physics-based constraints in their algorithms to achieve accurate predictions with limited training data.

"When you train the machine learning algorithm on specific grain
boundaries, you will get extremely high accuracy in the stress predictions
of these same boundaries," Chew said, "but the more important question
is, can the algorithm then predict the stress state of a new boundary
that it has never seen before?" Chew said, the answer is yes, and very
well in fact.

"What machine learning does for the field of mechanics of materials
is that it enables us to use data to make predictions quickly and
autonomously. This is a significant advancement over the development
of complicated and highly-specific physics-based models to make failure predictions," Chew said.

Measuring these grain boundary stresses is the first step towards
designing aerospace materials for extreme environment applications.

"Being able to establish quantitative descriptors of the boundaries
will enable scientists to engineer grain boundaries to be stronger,
and more heat and corrosion resistant," Chew said.

Cui stressed that the algorithm they have developed is very general and
can be used to quantify the atomic-scale stresses governing fracture
and failure processes in many other material systems.

This work was supported by Ali Sayir under the Aerospace Materials
for Extreme Environments program of the Air Force Office of Scientific Research.

========================================================================== Story Source: Materials provided by University_of_Illinois_Grainger_College_of_Engineering.

Original written by Debra Levey Larson. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Y. Cui, H.B. Chew. Machine-Learning Prediction of Atomistic
Stress along
Grain Boundaries. Acta Materialia, 2022; 222: 117387 DOI: 10.1016/
j.actamat.2021.117387 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211109120304.htm

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