Work

Autonomous laboratories for accelerated materials discovery: a community survey and practical insights

Year: 2024

Type: article

Abstract: We share the results of a survey on automation and autonomy in materials science labs, which highlight a variety of researcher challenges and motivations. We also propose a framework for levels of laboratory autonomy from L0 to L5.

Source: Digital Discovery

DOI: 10.1039/d4dd00059e

Authors: Linda Hung, Joyce A. Yager, Danielle Monteverde, Dave Baiocchi, Ha-Kyung Kwon, Shijing Sun, Santosh Suram

Institutions: Toyota Research Institute, University of Washington

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Toyota Research Institute

OpenAlex ID: W4399208173

FWCI: 3.122

Cites: 30

Cited by: 33

Related to: 10