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Do Self-Driving Laboratories Accelerate Discovery or Automate Uncertainty, Bias, and Irreproducible Decisions?

Year: 2026

Type: article

Abstract: Self-driving laboratories are increasingly presented as a route to faster, more systematic discovery by coupling algorithmic experiment selection with automated synthesis, testing, and analysis. Yet the same closed-loop structure that can improve experimental efficiency can also repeat hidden biases, amplify measurement error, misinterpret model confidence, and convert poorly specified objectives into reproducible but scientifically weak decisions. This realist review asks not whether laboratory

Source: Pharmacophore

DOI: 10.51847/i178allztd

Authors: Chen Hao, Liu Fang, Zhao Lin, Wei Zhang

Institution: Zhejiang University

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7203540765

FWCI:

Cites: 37

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