Efficient Exploration of Microstructure-Property Spaces via Active Learning

Efficient Exploration of Microstructure-Property Spaces via Active Learning

In materials design, supervised learning plays an important role for optimization and inverse modeling of microstructure-property relations. To successfully apply supervised learning models, it is essential to train them on suitable data. Here, suitable means that the data covers the microstructure...

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Bibliographic Details
Main Authors: Lukas Morand, Norbert Link, Tarek Iraki, Johannes Dornheim, Dirk Helm
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-02-01
Series:Frontiers in Materials
Subjects:
active learning
adaptive sampling
data generation
inverse modeling
materials design
membership query synthesis
Online Access:https://www.frontiersin.org/articles/10.3389/fmats.2021.824441/full

Internet

https://www.frontiersin.org/articles/10.3389/fmats.2021.824441/full

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