Data efficiency of classification strategies for chemical and materials design

We benchmark the performance of space-filling and active learning algorithms on classification problems in materials science, revealing trends in optimally data-efficient algorithms.

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Publication Details

Journal
Digital Discovery
Published
2024-12-03
DOI
https://doi.org/10.1039/d4dd00298a
Citations
3
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.35

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article

Data efficiency of classification strategies for chemical and materials design

Michael A. Webb, Quinn Gallagher
3 citations
Digital Discovery
Machine Learning in Materials Science
0.35
article

Data efficiency of classification strategies for chemical and materials design

Michael A. Webb, Quinn Gallagher
article en
3 citations

Abstract

We benchmark the performance of space-filling and active learning algorithms on classification problems in materials science, revealing trends in optimally data-efficient algorithms.

Digital DiscoveryVol. 4(1)
Princeton University (US)
National Science Foundation, Princeton University, Division of Materials Research
Openalex Percentile: Top 43%
Machine Learning in Materials Science
0.35
DOIOpen Access PDFOpenAlex
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Data efficiency of classification strategies for chemical and materials design — Michael A. Webb, Quinn Gallagher · Digital Discovery (2024) | TGRS Research Map | TGRS