Translate high-level mission goals (e.g., electrification, grid storage, decarbonization, critical mineral reduction) into specific functional materials objectives, target property boundaries, and composition constraints.

Architect end-to-end R&D maps integrating literature intelligence, physics-informed descriptors, virtual screening, robotic synthesis layouts, high-throughput characterization, and active learning loops.

Define structured schemas and data collection protocols to consolidate noisy experimental records from multiple laboratories, institutions, or operators into clean, normalized, AI-ready datasets.

Establish clear primary screening thresholds, secondary verification criteria, and uncertainty-reducing experiment plans to make sure laboratory resources target the most informative candidate compositions.

Package complex materials AI workflows, candidate decision maps, and screening strategies into concrete, professional technical summaries suitable for research proposal annexes and collaborative partnerships.