Autonomous Science

Cloud lab architecture: how remote, robotic wet-labs run experiments as a service - the experiment API, scheduling and queueing, workcell orchestration, sample logistics, isolation and reproducibility behind experiment-as-a-service in 2026.

Self-driving lab data provenance: experiment lineage, ELN/LIMS integration, versioned protocols and FAIR data - why replayability is the hard part of autonomous discovery, and the architecture that makes it reproducible in 2026.

Autonomous characterization architecture: how in-situ XRD, spectroscopy and imaging feed structured results back to the model in a self-driving lab - the perception layer, data reduction, uncertainty and the human-in-the-loop checks in 2026.

Scientific foundation models explained: pretraining on molecules, materials, and sequences, where the training data comes from, architectures, benchmarks, and limits in 2026.

Laboratory automation orchestration architecture: SiLA 2 device drivers, schedulers, experiment queues, and lab-as-code - the software layer that runs self-driving lab robots in 2026.

Machine learning interatomic potentials explained: MACE, foundation potentials, and GNoME-class screening that narrows the search before robots run experiments.