Matflow turns messy lab data from batteries, formulations, and materials R&D into working predictive models, cost-aware candidate rankings, and publication-grade dossiers — with every number traceable to its source, engine, and evidence class.
Hey Product Hunt! 👋
I'm thrilled to introduce Matflow — an open, AI-native R&D operating system built specifically for chemists, materials scientists, and experimental labs.
🔬 WHY WE BUILT MATFLOW
If you've ever worked in a wet lab or materials R&D, you know the pain:
• Experiments are scarce: A typical campaign yields 20–50 rows of data, not big-data millions. Standard deep learning falls apart.
• Candidates fail on economics: Promising molecules or alloys look great in computational theory, but get killed in the lab due to raw material costs, VOC limits, or scale-up kinetics.
• The "AI black box" problem: In science, an ungrounded hallucination isn't just a minor bug — it wastes months of bench time and thousands of dollars in reagents.
We built Matflow to give research teams an end-to-end OS that bridges raw lab data, physics engines, and active learning without the guesswork.
💡 WHAT MAKES MATFLOW DIFFERENT
🛡️ The Evidence Class Discipline
Nothing fake is ever presented as real. Every single data point in Matflow carries a verifiable evidence class: MEASURED (raw instrument export), COMPUTED (DFT / MD / PyBaMM), PREDICTED (ML model with split-conformal confidence intervals), EXTRACTED (literature), HYPOTHESIS (LLM suggestion), or DEMO. Tamper-evident audit logs mean you can trust your data for publication or patent filing.
⚡ Lab Instruments to Predictions in 5 Minutes
Upload messy CSVs, PDFs, or raw files from XRD, GC/MS, FTIR, NMR, and battery cyclers. Matflow normalizes the digital thread, runs auto-model selection (from Gaussian Processes to Chemprop), and generates Pareto-optimal recommendations balancing performance AND cost.
🤖 From Copilot to Self-Driving Lab
Use our AI copilot for DOE and literature extraction, tap into the MCP server directly from Claude/Cursor, or generate validated Opentrons robot protocols to run closed-loop experiments.
🔓 You Own Your Data & Models
Self-hostable via Docker, complete with ONNX/joblib model export, OPTIMADE v1.2 APIs, and RO-Crate dataset bundles. No vendor lock-in.
🚀 GETTING STARTED & PH COMMUNITY OFFER
• You can get started with our cloud version or spin it up locally with Docker.
• Free accounts get access to all modules, and academic domains (.edu / .ac) receive auto-approved research access.
• For the Product Hunt community, use code PRODUCTHUNT20 for 20% off any paid tier or credit pack.
We’d love to hear your feedback, feature requests, and thoughts on how AI should interact with the physical sciences! What instruments or workflows should we integrate next?