⚗️ FLUXMATERIA — CHEMISTRY

Catalyst-native screening
at 35 candidates per second

From a composition alone, rank candidates across eight industrial families, simulate the full catalytic cycle with activation barriers and turnover frequencies, solve steady-state microkinetics, and run inverse discovery under constraints. DFT-competitive accuracy with zero training data, zero fitted parameters, and microseconds per prediction.

8 industrial families 9-phase readiness scoring Full catalytic cycle + microkinetics Electrochemistry: potential + pH Mini-MKM composition-dependent rates Selectivity-aware operating window ASF product distribution (FT) Inverse discovery with constraint DSL
Open scoring benchmark Read inverse-discovery case study
93.4%
Pairwise ranking accuracy on the catalyst benchmark
91.7%
Top-1 accuracy on the same benchmark
0.667 eV
vs 28 primary-lit experimental points
0.236 eV
Activation-barrier MAE (29 cases)
35 / s
Full-stack throughput per candidate
0
Training data · 0 fitted parameters
The breakthrough

Catalyst screening at DFT accuracy, at ML speed.

DFT is honest but too slow to screen thousands of candidates. ML force fields are fast but degrade out-of-distribution and need millions of DFT points to train. Catalyst runs the full cycle — barriers, turnover, selectivity, microkinetics, electrochemistry — in microseconds per candidate with zero training data and zero fitted parameters. Same accuracy band as PBE-DFT on published benchmarks. Unlimited out-of-distribution reach.

What the Catalyst module gives you

From a single composition to an auditable catalyst decision package.

🏭

Family classification + readiness

Assigned to one of 8 industrial families (ammonia synthesis, Fischer-Tropsch, selective oxidation, WGS, HDS, reforming, electrocatalysis, hydrogenation) with a 0–100 readiness score.

📏

9-phase readiness breakdown

Activity, selectivity, thermal stability, poison resistance, regenerability, cost, supply-chain risk, toxicity, novelty — each scored independently so trade-offs are explicit.

🔁

Full catalytic cycle

Elementary steps, activation barriers, transition-state positions, Eyring rate constants, ΔH / ΔG / Keq, predicted turnover frequency, rate-limiting step flagged.

🌋

Sabatier volcano position

Descriptor binding energy placed on a family-specific volcano with weak / optimal / strong classification. Descriptor and peak resolved in the same engine pass for scale consistency.

⚙️

Steady-state microkinetics

Surface coverages, net elementary-step rates, gas-phase TOF, and Degree-of-Rate-Control sensitivity for every step in the cycle.

⚡

Electrocatalysis

Applied potential (vs SHE) and electrolyte pH propagate into proton-coupled electron-transfer barriers. HER / OER / ORR ready with PCET coverage physics.

📊

Operating window (T, P)

2D heatmap of TOF × selectivity over (T, P). Feasibility contour identifies zones where both activity and selectivity clear thresholds. ASF chain-growth α included for Fischer-Tropsch.

🔎

Inverse discovery

Constraint-driven search with 9 presets (from “cheapest ammonia catalyst” to “non-silver EO”) over 10 stoichiometry templates, Pareto visualisation, and novelty flagging.

The catalyst-native pipeline

Six passes from composition to audit-ready decision package.

1

Classify family

Composition in → industrial family out. Readiness score and family-specific descriptor selection locked in.

2

Simulate the cycle

Elementary steps, barriers, Eyring TOF, rate-limiting step. Three internally-consistent physics paths reported.

3

Solve microkinetics

Steady-state coverages, composition-dependent rates, DRC sensitivity. Captures Haber-Bosch pressure order and FT Langmuir inhibition.

4

Map the window

TOF × selectivity heatmap across (T, P). Feasibility contour. ASF distribution for FT candidates.

5

Band the uncertainty

Three TOF estimates (cycle-native / BEP / full MKM) report a method-spread band. Outliers flagged.

6

Package decision

Readiness + cycle + window + uncertainty + inverse-discovery suggestions exported as one audit-ready packet.

Why you can trust it

Validated against published benchmarks and primary literature, not internal splits.

93.4%
Pairwise ranking accuracy on the catalyst benchmark. The number that actually drives portfolio triage.
91.7%
Top-1 accuracy on the same benchmark — the single-best-candidate call across families.
0.667 eV
vs 28 primary-literature experimental points. Calibrated directly against measured activation energies, not model-to-model.
0.236 eV
Activation-barrier MAE across 29 cases — in the same band as PBE-DFT, at microsecond-per-call throughput.
35 / s
Full-stack throughput: family + cycle + microkinetics + operating window + uncertainty per candidate.
0
Training data. 0 fitted parameters. Unlimited out-of-distribution reach.

How FluxMateria compares

Head-to-head against the two dominant catalyst-modelling approaches.

CapabilityFluxMateriaPBE-DFTML force fields (MACE, GemNet)
Activation-barrier MAE0.236 eV0.20–0.30 eV0.10–0.20 eV
d-band descriptor MAE0.197 eV0.15–0.30 eV0.15–0.25 eV
Training dataNoneNone (full DFT per case)106–107 DFT points
Fitted parameters00millions
Time per predictionmicrosecondsCPU-hoursseconds (GPU)
Out-of-distributionUnlimitedUnlimitedDegrades
Full cycle + microkineticsBuilt-inManualNot provided
Inverse discovery9 presets + DSLNot providedNot provided

The key insight: DFT is the reference — but it can’t screen thousands of candidates in a week. ML force fields can, but they degrade the moment you leave the training distribution. Catalyst matches PBE-DFT on barriers and d-band descriptors at microsecond throughput, with unlimited out-of-distribution reach and no training data required. See the full benchmark →

Where Catalyst wins

Catalyst-discovery workflows where DFT is too slow and ML force fields are out-of-distribution.

Use case 1

Inverse discovery

“Cheapest ammonia catalyst”, “non-silver ethylene oxide”, “novel compositions” — 9 constraint presets, Pareto-front visualisation, literature-novelty flagging.

Use case 2

Fischer-Tropsch scouting

Predicted ASF α, full C1–C19+ distribution, bucketed to gasoline / diesel / wax. Co α ≈ 0.80, Fe α ≈ 0.84 — in the industrial range.

Use case 3

Electrocatalysis design

Applied potential + pH propagate into PCET barriers. HER / OER / ORR ready with metal-dependent Heyrovsky barriers and proper coverage physics.

Use case 4

Operating-window sizing

2D (T, P) heatmap of TOF × selectivity. Find zones where both activity and selectivity clear thresholds — before committing reactor time.

Use case 5

Supply-chain triage

Cost, supply-chain risk, toxicity, regenerability scored per candidate. Ship only the catalysts that survive the full 9-phase readiness filter.

Use case 6

Portfolio rebalancing

Rank an internal library against a new spec in seconds. Keep the full audit trail for the committee review.

Scope & Limitations

Strengths

  • Same-band accuracy as PBE-DFT on barriers and d-band descriptors, at microseconds per call.
  • Full cycle + microkinetics + electrochemistry + inverse discovery — not just a surrogate.
  • 93.4% pairwise ranking accuracy on the catalyst benchmark — the number that drives portfolio triage.
  • Zero training data, zero fitted parameters, unlimited out-of-distribution reach.
  • Method-spread uncertainty band from three internally-consistent physics paths per TOF prediction.

Known limitations

  • Scope is heterogeneous / electro- catalysis; homogeneous single-site catalysis is on the roadmap but not in v1.
  • Operating-window heatmaps use family-default selectivity models; bespoke selectivity physics requires a pilot workshop.
  • Inverse discovery over 10 stoichiometry templates covers the common industrial families; exotic scaffolds need explicit seeding.
  • ASF product distribution shipped for Fischer-Tropsch; other chain-growth regimes (methanol-to-hydrocarbons) are on the roadmap.

Catalyst decisions deserve catalyst-native reasoning.

Pilot access includes Catalyst, the full Chemistry stack, MechanismOS, and a Workspace seat for audit-ready runs.

Request Pilot Access →