Scientific Engineering of Self-Driving Laboratories at Scale.
other · Level V
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- Record sourced from PubMed, PMID 42708919.
- Also identified by DOI 10.1021/acsnano.6c15286.
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Abstract
Scaling self-driving laboratories expands the space of discoverable phenomena but also increases operational complexity, semantic heterogeneity, and decision dependencies that can erode controllability, interpretability, and functional coherence. We define this scale-induced disorder as system entropy and have developed an engineering framework to manage it in AIchem. Hardware-software codesign, layered modularity, capability abstraction and skill encapsulation organize heterogeneous research objects, instruments, computational tools, and algorithms into programmable, composable units linked by closed-loop task and data flows. AIchem spans more than 2,600 m2, with 605 registered workstations supporting batteries, catalysis, biochemistry, and functional materials and has handled more than 10,000 research task dispatches. These deployment and use measures outline a practical route for building intelligent research infrastructure at scale.