Applying artificial intelligence to materials research requires abundant curated experimental data and the ability for algorithms to request new experiments. ESCALATE (Experiment Specification, Capture and Laboratory Automation Technology)—an ontological framework and open-source software package—solves this problem by providing an abstraction layer for human- and machine-readable experiment specification, comprehensive and extensible (meta-) data capture, and structured data reporting. ESCALATE simplifies the initial data collection process, and its reporting and experiment generation mechanisms simplify machine learning integration. An initial ESCALATE implementation for metal halide perovskite crystallization was used to perform 55 rounds of algorithmically-controlled experiment plans, capturing 4336 individual experiments.