Universal Chemistry & Materials Scientific R&D OS

Accelerate materials & chemistry R&D with artificial intelligence

Matflow turns sparse experimental data from any chemistry or materials domain into a working model, ranks candidates by predicted performance and cost, and tells you which experiments deserve your lab — with a research AI that reads your literature. Every number carries an evidence label and the live results are published on the benchmarks page.

◆ Interactive Studio Preview
DISCOVERY RUNNER|Demo: 1,420 illustrative candidates|Ensemble R²: 0.942|Conformal Range: ± 3.8% (90% Conf)
Multi-Objective Response Surface
Performance (score) vs Production Cost ($/kg)
Pareto Optimal Dominated
Illustrative Pareto frontier of material candidates: performance vs cost40%70%95%$0/kg$5/kg$10/kgCF-712 Ti-6Al-4VCF-849 NMCSiC ceramic
★ Top Pareto Candidate Shortlist
CF-849LiNi0.8Mn0.1Co0.1O2 (Battery)
Perf: 88.4%Stab: 94.2%Cost: $4.82/kg
TOPSIS Rank #1
CF-712Ti-6Al-4V (Alloy)
Perf: 81.6%Stab: 91.8%Cost: $2.15/kg
TOPSIS Rank #1
CF-503PMMA/PLA blend (Polymer)
Perf: 85.9%Stab: 92.4%Cost: $6.40/kg
Score 0.88
SHAP Local Feature Decomposition
Target: Performance
Cation mixing
+14.2%
Crystallite size
+8.6%
Sintering temp
-3.1%
Conformal calibration passes 90% empirical test coverage with zero extrapolation penalty.
5-Minute Discovery
Instant web execution
20+ Quality Checks
Rigorous statistical validation
TEA In The Loop
CapEx / OpEx aware candidates
100% Free For Academics
No sales calls or NDA needed
Validated on held-out data — every number reproducible on the benchmarks page
0.41–0.95
Held-out R² across public benchmark datasets
published
0.95
Leave-family R² on the bundled demo corpus
published
4
Material systems validated against lab measurements
published
6
Public gold suites, reproducible end to end
published
1.0
Instrument gold suite pass rate
published

Why materials R&D is stuck

Three bottlenecks that every experimental campaign hits — and that Matflow is built to break.

Experiments are scarce

A typical R&D campaign produces tens of rows, not thousands. Every model built on that data is data-starved before it starts — and most platforms ignore it.

Candidates are expensive

Promising ideas fail for reasons that were never measured — raw-material cost, equipment scale, stability. Economics kill as many materials as kinetics do.

Trial and error is slow

One variable at a time, one campaign per quarter. The design space of compositions × conditions is far too large to sweep by hand.

◇ End-to-End Workflow

The 5-Stage Discovery Loop

Stage 04Multi-Objective Pareto

Optimization Module

Searches multi-dimensional composition spaces under strict physical constraints (100% formulation sum) to uncover non-dominated Pareto frontiers.

TOPSIS RankingPareto Dominance100% Sum Constraints
Interactive Preview● Illustrative demo data
Pareto Frontier: Performance vs Production Cost ($/kg)
0 $/kg3 $/kg6 $/kg9 $/kg12 $/kg40557085100
● Gold: Pareto Optimal (TOPSIS Ranked)● Gray: Dominated candidates

Every number carries an evidence label

No hidden black boxes: each result is classified by how it was produced, and one click opens the engine card — name, version, license, limitations, citation.

MEASUREDRaw experimental / instrument result
COMPUTEDReal engine run (M3GNet, PyBaMM, pycalphad, …)
PREDICTEDRegistered model output with uncertainty
EXTRACTEDFrom literature — needs review
HYPOTHESISLLM / generative proposal
DEMOSynthetic, heuristic, or demo
◆ Platform Comparison

What actually separates Matflow from legacy enterprise AI

Citrine, Uncountable, and Intellegens run quote-only enterprise sales — a demo, a proposal, and months of procurement before you see a price. Matflow publishes self-serve pricing you can check right now. Every result the platform returns ships with an evidence label and a citation back to the engine that computed it, not a bare number — and the toolbox below spans 22 materials and chemistry domains in one platform, from formulations to DFT to reactor engineering.

Self-serve
Instant Access
Vs 3–6 months sales delay
6 Gold Suites
Regression-Gated
DataMap · Atlas · Instrument · Agent · Sci-Record · Base
Engine Cards
Real vs Surrogate
Every number traceable to its engine + limitations
Free Tier
Academic Access
Open to researchers globally

A Unified Platform for Materials & Chemistry R&D

Built by researchers, for researchers — the AI and ML run in the background so you can focus on the science.

Generate

Turn a handful of experiments into a statistically faithful dataset — correlations preserved, not invented.

Evaluate

Score synthetic data against the real thing with 20+ metrics, distribution overlays and correlation similarity before you trust it.

Predict

Train an explainable ensemble and watch predicted performance respond in real time, with confidence bands and an extrapolation flag.

Optimize

Search the design space for Pareto-optimal candidates that balance performance, stability and cost — diverse and ready for the lab.

The full toolbox

Every stage of the workflow, from raw data to techno-economic analysis — across domains.

Domain studios

Data & modeling

Lab & automation

Decisions & evidence

From Data to Decisions in 4 Stages

A streamlined four-stage workflow designed for experimental materials scientists.

01

Data Synthesis

Turn sparse experiments into statistically faithful datasets — columns auto-mapped and QC-checked, correlations preserved, not invented.

02

Evaluation

Score synthetic data against the real thing with 20+ metrics, distribution overlays and correlation similarity before you trust it.

03

Prediction

Train an explainable ensemble and watch predicted performance respond in real time, with confidence bands and an extrapolation flag.

04

Optimization

Run DOE or active learning and rank candidates by performance AND cost — Pareto-optimal, diverse, and ready for the lab.

Explainability, not black boxes

You see why the model believes what it believes

Every prediction comes with Global and Local Impact explanations, conformal prediction intervals, an extrapolation flag when the model is outside its training envelope, and an engine card naming exactly which engine produced the number and what it can't do. A number the model is guessing at looks different from one it has real support for.

Scientific Validation

Validated the way a research tool should be

Matflow is validated against held-out rows the models never trained on, run through the production pipeline end to end: real engines benchmarked against the surrogates they replaced, six gold suites with deterministic pass gates, and a base-model program with grouped and temporal splits — every number downloadable as a reproducibility bundle.

Atomistic Surface & DFT Energy Landscape|Facet: Host Oxide (001) Slab|Substitution Energy: +0.32 eV (vs 0.00 eV host)
Atomistic Crystal SurfaceStep 3 of 4: Transition State (ΔE‡)
AAAABAADiffusion TS: ΔE‡ = 0.42 eV
A Host Atom B Substituted Atom O Oxygen Adatom
DFT Energy Landscape (ΔE)
Reference Lattice vs Matflow A-Substituted Lattice
0.0 eV+0.8 eV+1.5 eVReference Lattice (1.08 eV)Matflow A-Substituted (0.42 eV) ★
61% Lower Diffusion Barrier: A-site substitution strains the host lattice and softens the migration path, enabling adatom hops at 400°C instead of 600°C while suppressing vacancy clustering.
3. Transition State (ΔE‡): Saddle point for surface diffusion; the barrier ΔE‡ drops from 1.08 eV to 0.42 eV after A-site substitution.ΔE‡(A-sub) = 0.42 eV vs ΔE‡(ref) = 1.08 eV

Frequently Asked Questions

The questions researchers ask most — the full help center is here.

Bring your experiments. Leave with candidates.

Matflow is a professional research platform: Data Synthesis → Evaluation → Prediction → Optimization — every stage explainable, every number reproducible.