Self-Driving Laboratories
A self-driving laboratory (SDL) combines automated instruments with closed-loop machine learning: a model of the experiment is updated as data arrive and is used to choose the next experiment, so that informative data are gathered with fewer runs. The basic structure is an iterative loop of design, measurement and analysis; strategies such as design of experiments, active learning and Bayesian optimization differ mainly in how the next experiment is selected and when the loop stops. Building and operating full SDLs is costly and demands skills in domain science, programming and data science, which has motivated small, low-cost instruments for teaching and for prototyping algorithms.
Claude-Light as a low-cost prototyping instrument (2025)
Kitchin introduced Claude-Light, a remotely accessible RGB LED and ten-channel photometer controlled by a Raspberry Pi behind a REST API, as a real but noisy instrument for practicing automation (Kitchin 2025). Using it, he illustrated a hierarchy running from HTTP calls and loops, through a Latin square design, to a Gaussian process model inverted by optimization, and then to LLM-driven instrument selection, tool calling and code generation. The device ran more than 73,000 experiments for more than 1800 unique IP addresses from about August 2024, including classes of about 50 simultaneous students. The paper stresses that reliable uncertainty estimates (helped by output scaling and a white-noise kernel) are essential when uncertainty drives the choice of experiments, and that a single instrument is still far from a lab of interacting instruments. Summary: kitchin2025b.
Claude-Light data for generative inverse design (2025)
Kitchin used 100 Latin hypercube measurements from Claude-Light to compare ways of choosing LED settings that hit target sensor counts, showing that generative models conditioned on the desired outputs recover the same settings as forward-model optimization or a backward model, with sample-based uncertainty (Kitchin 2025). He presents the instrument data as a prototype for formulation-style problems with several inputs and several properties, and notes that how to choose experiments efficiently for training generative models, unlike for forward models in active learning, remains open. Summary: kitchin2025a.
Autonomous laboratories in the agentic science perspective (2025)
In a Comment on agentic science, Xin, Kitchin and Kulik describe a continuum of laboratory automation from robotics to LLM-driven agents (Xin et al. 2025). They cite Coscientist, which turns natural-language requests into procedures and runs cloud laboratory equipment through APIs, and A-Lab, an autonomous solid-state synthesis lab combining robotics, conventional ML and ab initio calculations; they classify A-Lab as not agentic because its LLM only predicts synthesis protocols outside a closed reasoning loop. They argue that autonomous experimentation needs safety interlocks, such as halting a plan that calls for a nonexistent compound or violates safety constraints, and note AFMBench as an early benchmark that exposed failure modes of LLM agents in real microscopy tasks. Summary: xin2025.
Accessibility, deployment models and roadblocks (2025)
In a Perspective with many SDL developers, Canty et al. define an SDL as digital tools that predict, propose experiments and update beliefs coupled to automated hardware that carries them out, and argue that what separates SDLs from high-throughput or cloud labs is the selection of experiments, adaptation of methods and integration of tools into workflows (Canty et al. 2025). They compare centralized SDL facilities with distributed networks of group-level SDLs and favor hybrids of the two (see democratization of research). They list roadblocks including scarce, poorly documented vendor APIs, fragmented middleware (ROS, SiLA 2, BlueSky) adopted mainly by the most advanced groups, the need to encode constraints and make long-term objectives measurable, and the need to measure experimental uncertainties directly. They recommend benchmarking and honest reporting of platform performance, and a consortium and institute to maintain standards, software and training. Summary: canty2025.