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Live Demo | MU-StarSeeker: Agentic AI for Frontier R&D Workflows

May 27, 2026 views 40

MU-StarSeeker in MU-3.0 release expands Molecular Universe from AI-assisted research workflow to agent-managed materials discovery automation, from molecule search and formulation optimization to cell performance prediction and manufacturing quality guidance.

Traditional battery R&D is slow, fragmented, and resource-intensive. Developing and commercializing a new chemistry could take 10 years to go from lab-scale R&D through A-sample, B-sample, C-sample and finally SOP, involving tedious manual iterations across materials search, formulation, simulations, testing, and validation. Starting with MU-3.0 release, MU-StarSeeker is built to disrupt that traditional model by turning isolated research steps into automated and continuously learning agent-managed workflow.

Key Advances in MU-3.0 Include:
1. MU-StarSeeker, Agent-Managed Material Discovery Workflow Automation

2. MU-3.0 Supports Both Lithium and Sodium Chemistries

3. Closed-Loop Dry and Wet Data Through Autonomous Labs (“A-Labs”)