EigenMind builds autonomous AI agents that work alongside your researchers, from hypothesis generation to experimental design, compressing years of discovery into weeks.
Materials science breakthroughs take decades. We're building AI agents that understand physics, chemistry, and domain literature, and can write and execute code on the fly, to dramatically shorten the path from idea to new material.
Purpose-built AI agents for every stage of the materials discovery pipeline.
Agents that read, understand, and synthesize thousands of scientific papers to surface relevant findings and knowledge gaps.
Automatically generate novel, testable hypotheses by connecting insights across disciplines, chemistry, physics, and materials science.
AI-driven experimental planning that optimizes for cost, time, and information gain using Bayesian and active learning strategies.
Predict material properties from composition and structure using state-of-the-art surrogate models and physics-informed ML.
Autonomous agents that iteratively propose, evaluate, and refine candidates, tightening the design-test-learn cycle.
Seamlessly connect to your internal databases, ELNs, and public repositories. Agents work with your existing data infrastructure.
Agents dynamically write and run Python, simulation scripts, and analysis pipelines in sandboxed environments, no manual coding required.
Tell our agents what material properties you need, thermal conductivity, band gap, corrosion resistance, or any target specification.
Our agents autonomously search literature, query databases, run simulations, and build predictive models to map the design space.
Receive ranked candidate materials with full reasoning traces, confidence intervals, and suggested experimental validations.
Feed experimental results back in. Agents learn, refine, and propose the next round, closing the loop toward your target material.
See how EigenMind's AI agents can accelerate your materials research. Fill out the form and our team will reach out within 24 hours.
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