⚛️ FLUXMATERIA — CHEMISTRY

Every bond in the periodic table,
at 0.08% error.

Bond lengths for 453 validated bonds at 0.079% mean error. Bond dissociation energies for 908 validated BDEs at 0.289% mean error. 64 elements spanning p-, d-, and s-block. 2,080 predictable element pairs. Sub-millisecond per query, zero fitted parameters.

64 elements 453 bonds 908 BDEs 2,080 pairs Sub-ms query
Open chemistry benchmark Read the MechanismOS case study
1,361
Validated bond observables (lengths + energies)
0.079%
Bond-length MAPE across 453 bonds
0.289%
Bond-energy MAPE across 908 BDEs
64
Elements — p-block + d-block + s-block
0
Fitted parameters
The breakthrough

First-principles accuracy at lookup-table speed.

Empirical handbooks are exact but frozen — they can’t predict unmeasured bonds or show you the trend. DFT and CCSD(T) predict anything but take hours per bond. The Chemistry engine gives you both: first-principles bond lengths and energies for any of the 2,080 element pairs, sub-millisecond per query, with zero fitted parameters. Every term is auditable.

What Chemistry computes

Foundational bond data that everything downstream — mechanism, synthesis, spectroscopy — rides on.

📏

Bond lengths

453 validated bonds — 391 single + 62 multiple (aromatic, double, triple). 0.079% mean error against experimental data.

⚡

Bond dissociation energies

908 validated BDEs across singles, doubles, and triples. 0.289% mean error. 870 / 906 within 1.0%.

📐

Bond angles

Geometric descriptors for covalent networks. Feeds 3D structure construction in the Chemistry and Mechanism modules.

🧪

Reaction enthalpies

Hess’s-law ΔH from BDE differences. Covers transition-metal, p-block, and s-block bond-forming and bond-breaking steps.

🔎

Novel-bond prediction

2,080 element pairs — if a bond can exist between two elements in the supported set, the engine predicts length and energy in milliseconds.

📦

Batch & trend queries

Sweep an entire row or column of the periodic table in one call. Continuous model, so trends are smooth and visualisable.

🔌

API & downstream feed

Bond data flows directly into MechanismOS, Synthesis, Spectroscopy, and 3D-structure construction — one source of truth across the platform.

📒

Export & audit

Every prediction traces to a closed-form expression. CSV / JSON export, every run re-runnable bit-for-bit.

How a query is handled

Element pair in, bond length and energy out — in under a millisecond.

1

Query

Enter a bond type (C–H, Fe=O, N#N, C:C aromatic) or a raw element pair. Single call or batch over a list.

2

Compute

The engine evaluates the closed-form expression for length and energy — deterministic, sub-millisecond, no optimisation loop.

3

Compare

Return value alongside the experimental reference (where one exists) and the error percentage.

4

Export or chain

Download the catalog, feed downstream (MechanismOS ΔH, Synthesis route planner, Spectroscopy force constants), or log to Workspace.

Why you can trust it

Validated against curated experimental datasets — CRC Handbook, NIST, Morse R2PI high-precision BDEs.

0.079%
Bond-length mean error across 453 validated bonds. Only 4 outliers above 0.5% (I-O, As-C, F-Br, C-P).
0.289%
Bond-energy mean error across 908 validated BDEs. 870 / 906 within 1.0%, 887 / 906 within 1.5%.
1,361
Total validated observables — lengths + energies across singles, doubles, triples, and aromatic.
64
Elements covered — 24 p-block + 30 d-block + 10 s-block. 2,080 total element pairs computable.
Sub-ms
Per-query latency on a single CPU. Same engine handles batches of thousands of bonds per second.
0
Fitted parameters. Every prediction traces to a closed-form expression; same input returns the same output every time.

How FluxMateria compares

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

MetricFluxMateriaDFT (B3LYP)CCSD(T)Empirical tables
Bond-length error0.079%1–3%~0.5%0% (where listed)
Bond-energy error0.289%3–10%1–3%0% (where listed)
Latency per bond< 1 msMinutes to hoursHours to days< 1 ms (lookup)
Validated coverage1,361 observablesTypically < 50 per study< 20 per study~300 (fixed catalog)
Predict new bonds2,080 pairs, instantHours per bondDays per bondNot possible
Trend analysisYes (continuous)Yes (re-run series)Yes (re-run series)No (discrete)
Training dataNoneNoneNoneData is the tool
Auditable formulasClosed-form per bondMethod traceMethod traceDataset pointer

The key insight: Empirical tables are exact but frozen — they can’t 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-table speed across 2,080 element pairs — and every prediction is auditable. See the full benchmark data →

Where Chemistry wins

Workflows where having every bond on tap — not just the ones in the textbook — changes what’s tractable.

Use case 1

Mechanism ΔH in one call

Bond-breaking + bond-forming balances for any proposed mechanism step. Feeds MechanismOS activation-barrier computation directly.

Use case 2

Synthesis disconnection

Route planning uses FLUX-derived BDE gaps to rank disconnections by thermodynamic feasibility — no pre-trained retrosynthesis model required.

Use case 3

Novel-bond prediction

Proposed a compound with a bond that doesn’t exist in the handbook? Get length + energy + comparison to neighbouring element pairs in milliseconds.

Use case 4

Trend visualisation

Sweep an entire periodic-table row or column. The continuous model produces smooth length / energy trends for teaching, publication, and intuition.

Use case 5

3D structure & spectroscopy

Same bond-length / angle / force-constant values used everywhere downstream — 3D builder, IR / Raman, and MechanismOS all read the same source of truth.

Use case 6

Audit & teaching

Every prediction traces to a closed-form expression. Reviewers, examiners, and students can see why the number is what it is.

Chemistry in the product

Real captures from the live application. Click any image to zoom.

Bond-length query interface with element-pair input, predicted length, experimental reference, and error percentage
Bond queryElement pair in, bond length + experimental reference + error percentage out — in a single sub-ms call.
Bond dissociation energy table with per-bond values, experimental references, and error highlighting
BDE table908 validated dissociation energies by element pair — sortable, filterable, exportable.
Continuous-model trend sweep across a periodic-table row showing smooth length and energy variation
Periodic trend sweepContinuous-model sweeps across a periodic-table row or column — smooth curves, not discrete handbook jumps.
Export panel with CSV, JSON, Excel, PDF options and engine version stamp
ExportCSV / JSON / Excel / PDF export with engine version stamp for audit-grade reproducibility.

Scope & Limitations

Strengths

  • 64 elements spanning p-, d-, and s-block — 2,080 predictable element pairs.
  • 0.079% bond-length and 0.289% bond-energy mean error — handbook-scale coverage at first-principles accuracy.
  • Continuous model: smooth trends across rows, columns, and series, not discrete handbook jumps.
  • Sub-millisecond per query — batch sweeps of thousands of bonds in seconds.
  • Feeds MechanismOS, Synthesis, Spectroscopy, and 3D-structure modules from one source of truth.

Known limitations

  • Scope is singles, doubles, triples, aromatic. Metal-metal quadruple bonds and exotic cluster compounds sit outside the current model.
  • A handful of known outliers above 5% (Hg-I, K-Rb, Rb-Cs, Be2) — flagged in the benchmark data, acceptable for screening but not for absolute reporting.
  • Radioactive / post-actinide elements are not in the 64-element panel.
  • For coordination-chemistry nuances (oxidation-state-dependent bond lengths) use the Materials module for periodic solids.

Query any bond. See the prediction.

Pilot access includes the Chemistry bond engine, the full MechanismOS + Synthesis + Spectroscopy stack, Advanced Methods, and a Workspace seat for audit.

Request Pilot Access →