FLUXMATERIA — CHEMISTRY

Every bond, from first principles.

Bond lengths and bond dissociation energies for 64 elements. 1,361 validated observables. First principles, not fits. Sub-millisecond per query.

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Capabilities

📏

Bond lengths

453 validated bonds: single, double, triple, aromatic. 0.079% mean error vs experimental data.

Bond dissociation energies

908 validated BDEs across singles, doubles, and triples. 0.289% mean error. 870/906 within 1%.

🧪

Reaction enthalpies

Hess’s law from BDE differences. Coverage for transition metal, p-block, and s-block bonds.

🔍

Batch screening

Query thousands of bonds per second. Export full catalogs. 2,080 element pairs computable.

🎯

3D structure support

Bond lengths feed directly into molecular geometry construction for 3D visualization.

📊

Full audit trail

Every prediction includes the computed value, experimental reference, error percentage, and data provenance.

How FluxMateria compares

Head-to-head against every major approach to bond properties

Metric FluxMateria DFT (B3LYP) CCSD(T) Empirical Tables
Bond length error 0.079% 1–3% ~0.5% 0% (where listed)
Bond energy error 0.289% 3–10% 1–3% 0% (where listed)
Speed per bond <1 ms Minutes to hours Hours to days <1 ms (lookup)
Validated coverage 1,361 observables Typically <50 per study <20 per study ~300 (fixed catalog)
Predict new bonds 2,080 pairs, instant Hours per bond Days per bond Not possible
Trend analysis Yes (continuous model) Yes (re-run series) Yes (re-run series) No (discrete values)
Physical model First-principles (0 fitted) First-principles First-principles None (raw data)

The key insight: Empirical tables are exact but frozen — they cannot predict unmeasured bonds or reveal trends. DFT and CCSD(T) can predict anything but cost hours to days per bond. FluxMateria combines first-principles accuracy with lookup speed across 2,080 element pairs. See full benchmark data →

At a glance

453
Validated bond lengths
391 single + 62 multiple
908
Validated bond energies
Singles + doubles + triples
64
Elements covered
p-block + d-block + s-block
0
Fitted parameters
Pure first principles

Typical workflow

1

Query

Enter a bond type (e.g. C–H, Fe=O, N#N) or element pair

2

Compute

Engine returns length (pm) and energy (eV) with experimental reference

3

Compare

See FLUX prediction vs experimental value with error percentage

4

Export

Download full catalogs or feed into downstream modules

Feeds into other modules

Bond data is foundational — it powers multiple downstream capabilities.

⚗️ Mechanism Discovery

Activation barriers computed from BDE differences. 908 bonds provide comprehensive coverage for Hess’s law calculations.

🧪 Synthesis Planning

Route planning uses FLUX-derived barriers. Bond energies determine thermodynamic feasibility of disconnections.

📐 3D Structure

Molecular geometry construction uses engine bond lengths as primary source. Covers aromatic, double, and triple bonds.

📈 Spectroscopy

IR and Raman predictions require accurate bond force constants, derived from bond length and energy curves.

Try the bond engines

Query any bond. See the prediction. Compare against experiment.

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