Zobrazeno 1 - 10
of 286
pro vyhledávání: '"Holm, Elizabeth A"'
In this work, we investigate the shape evolution of rotated, embedded, initially cylindrical grains (with [001] cylinder axis) in Ni under an applied synthetic driving force via molecular dynamics simulations and a continuum, disconnection-based grai
Externí odkaz:
http://arxiv.org/abs/2408.14752
Recent grain growth experiments have revealed that the same type of grain boundary can have very different mobilities depending on its local microstructure. In this work, we use molecular dynamics simulations to quantify uncertainty in the reduced mo
Externí odkaz:
http://arxiv.org/abs/2211.08640
Autor:
Amano, Nicholas a, 1, Lei, Bo b, 1, Müller, Martin c, d, Mücklich, Frank c, d, Holm, Elizabeth A. a, ⁎
Publikováno v:
In Materials Characterization February 2025 220
Publikováno v:
In Scripta Materialia 1 February 2025 256
Autor:
Choudhary, Kamal, DeCost, Brian, Chen, Chi, Jain, Anubhav, Tavazza, Francesca, Cohn, Ryan, WooPark, Cheol, Choudhary, Alok, Agrawal, Ankit, Billinge, Simon J. L., Holm, Elizabeth, Ong, Shyue Ping, Wolverton, Chris
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identi
Externí odkaz:
http://arxiv.org/abs/2110.14820
Autor:
Cohn, Ryan, Holm, Elizabeth
Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to its apparent stochastic nature. In this study, we generate a large dat
Externí odkaz:
http://arxiv.org/abs/2110.09326
Publikováno v:
Acta Materialia, 2021
Atomistic simulations provide the most detailed picture of grain boundary (GB) migration currently available. Nevertheless, extracting unit mechanisms from atomistic simulation data is difficult because of the zoo of competing, geometrically complex
Externí odkaz:
http://arxiv.org/abs/2108.09340
Publikováno v:
Acta Materialia, 2021
It has been hypothesized that the most likely atomic rearrangement mechanism during grain boundary (GB) migration is the one that minimizes the lengths of atomic displacements in the dichromatic pattern. In this work, we recast the problem of atomic
Externí odkaz:
http://arxiv.org/abs/2101.12026
We propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized
Externí odkaz:
http://arxiv.org/abs/2101.01585
Autor:
Cohn, Ryan, Holm, Elizabeth
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled data sets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high performance un
Externí odkaz:
http://arxiv.org/abs/2007.08361