A critical examination of active learning workflows in materials science

Digital Discovery 2026 Open access

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Authors: Akhil S Nair, Lucas Foppa
DOI: 10.1039/d6dd00081a
Cited by: 3
References: 156

Abstract

Data-centric materials science requires carefully designed active learning workflows, but existing practices remain limited by weak design frameworks and generic implementations.

Fields of Study & Subjects

Machine Learning in Materials Science Machine Learning and Algorithms Advanced Electron Microscopy Techniques and Applications

Publication details

Journal / source
Digital Discovery
Publisher
Royal Society of Chemistry (RSC)
Volume
5
Issue
6
Pages
2366–2382
Publication date
1 January 2026
Language
EN