Toward a High-Throughput Automated Materials Experimentation Platform: Hybrid-Automata-Inspired Modeling and Equipment Configuration Optimization

This paper presents an experimental-procedure-driven method for modeling and equipment quantity configuration of high-throughput automated materials experimentation platforms. The method integrates a hybrid-automata-inspired process model, a structured equipment dictionary, and experimental-procedure-to-equipment mapping to construct a unified platform model for timing and dependency analysis under practical constraints. Based on this model, equipment quantity configuration is formulated as a constrained integer nonlinear optimization problem and solved using an integer-coded differential evolution algorithm. A thermal insulation coating case study demonstrates the modeling procedure and resulting configuration scheme. The experiments show reduced operation time relative to manual execution and smaller standard deviations in coating transmittance. This study focuses on equipment quantity configuration rather than full platform layout and integration design.