Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

Predicting a priori local defects in amorphous materials remains a grand challenge. Here authors combine a rotationally non-invariant structure representation with deep-learning to predict the propensity for shear transformations of amorphous solids for different loading orientations, only given the...

Full description

Bibliographic Details
Main Authors: Zhao Fan, Evan Ma
Format: Article
Language:English
Published: Nature Publishing Group 2021-03-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-21806-z
id doaj-fbaccc18a78f426ab79a24a83fc9e2fd
record_format Article
spelling doaj-fbaccc18a78f426ab79a24a83fc9e2fd2021-03-11T11:33:54ZengNature Publishing GroupNature Communications2041-17232021-03-0112111310.1038/s41467-021-21806-zPredicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learningZhao Fan0Evan Ma1Department of Materials Science and Engineering, Johns Hopkins UniversityDepartment of Materials Science and Engineering, Johns Hopkins UniversityPredicting a priori local defects in amorphous materials remains a grand challenge. Here authors combine a rotationally non-invariant structure representation with deep-learning to predict the propensity for shear transformations of amorphous solids for different loading orientations, only given the static structure.https://doi.org/10.1038/s41467-021-21806-z
collection DOAJ
language English
format Article
sources DOAJ
author Zhao Fan
Evan Ma
spellingShingle Zhao Fan
Evan Ma
Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
Nature Communications
author_facet Zhao Fan
Evan Ma
author_sort Zhao Fan
title Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
title_short Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
title_full Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
title_fullStr Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
title_full_unstemmed Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
title_sort predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2021-03-01
description Predicting a priori local defects in amorphous materials remains a grand challenge. Here authors combine a rotationally non-invariant structure representation with deep-learning to predict the propensity for shear transformations of amorphous solids for different loading orientations, only given the static structure.
url https://doi.org/10.1038/s41467-021-21806-z
work_keys_str_mv AT zhaofan predictingorientationdependentplasticsusceptibilityfromstaticstructureinamorphoussolidsviadeeplearning
AT evanma predictingorientationdependentplasticsusceptibilityfromstaticstructureinamorphoussolidsviadeeplearning
_version_ 1724225452870991872

Similar Items