Matflow is an AI-powered scientific R&D platform for chemistry and materials.It turns small, sparse experimental datasets into actionable, high-potential candidates — cutting the time and cost of traditional trial-and-error R&D.
The short version
Matflow is a web-based, no-code platform that turns small, sparse experimental datasets into actionable candidates. You upload tabular experimental data; the platform handles the AI/ML in the background — data synthesis, evaluation, prediction, and optimization — and returns a ranked set of candidate recipes ready for the lab.
The four-stage workflow
Every project follows the same pipeline, and each stage runs standalone or end to end:
Data Synthesis — expands a small real dataset into a larger, statistically faithful synthetic one.
Evaluation — scores how well the synthetic data matches the real data, before you trust it.
Prediction — trains an explainable model that predicts material or chemical performance from conditions.
Optimization — searches for new high-performance, low-cost candidate recipes, ranked along a Pareto front.
On the Full Pipeline page, each stage's output automatically becomes the next stage's input. Every stage produces a job you can track under Runs.
Who it is for
Scientists, researchers and engineers in materials science, chemistry and chemical engineering who have experimental data and want to get more out of it — no machine-learning expertise required.
Ready for your first run? See Running your first run below, or the Tutorials section for a guided walkthrough.