Deep Learning
+ First Principle
R&D Is Still Experience-driven and Experiment-based
Long Duration
1-2 experiments per day 10 years from lab to production
High Cost
1 lab-scale success with a new product can cost $2-20 million
Long Duration
55% experimental data is unstructured
50% experiments are unrepeatable
70% of researchers struggle to repeat results
Traditional R&D tools trade precision for efficiency
Agent Mira for Materials R&D
AI Scientist scales materials innovation through Three Engines working as one loop.
Generative AI
Scale up exploration space
Generative AI expands the searchable design space by rapidly proposing large volumes of hypotheses, including new molecules, crystals, formulations, and chemical reactions. It turns early-stage ideation from manual guesswork into scalable candidate generation.
Exploration
Agentic Workflow
Scale up adaptive screening
Agentic workflows decompose complex R&D goals, select the right tools, and coordinate simulation, prediction, literature, and experimental feedback. This enables adaptive screening at scale, where each result informs the next decision instead of following a fixed linear pipeline.
Orchestration
High-throughput Lab
Scale up real-world iteration
High-throughput laboratories increase the speed of real experiments, data acquisition, and validation loops. By scaling iteration count and real-world feedback, they convert AI-generated and agent-selected hypotheses into reliable experimental evidence faster.
Throughput
Scale Up Innovation
Exploration Space
Adaptive Screening
Real-world Iteration
