Exploring self-driving labs for optoelectronic materials

Self-driving laboratories (SDLs), by combining automation with machine learning-guided experiment selection, have the potential to transform experimental mateirals science. To date, most SDLs have been optimisation-driven, designed to rapidly converge...

Authors

Jonathan J. S. Scragg (ORCID: https://orcid.org/0000-0001-8686-8721)

Publication Details

Journal: Faraday Discussions
Published: 2026-01-01
DOI: https://doi.org/10.1039/d6fd00020g
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 0.00

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Exploring self-driving labs for optoelectronic materials

Jonathan J. S. Scragg

Jan 1, 2026

Faraday Discussions

Machine Learning in Materials Science

article

Exploring self-driving labs for optoelectronic materials

Jonathan J. S. Scragg

January 1, 2026article en

Abstract

Faraday Discussions

Openalex Percentile: Top 12%

Machine Learning in Materials Science

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Exploring self-driving labs for optoelectronic materials — Jonathan J. S. Scragg · Faraday Discussions (2026) | TGRS Research Map | TGRS