Frequently Asked Questions

FluxMateria is physics-only - no ML or AI. Predictions are grounded in molecular physics rather than statistical correlations, which improves extrapolation in some regimes (see Benchmarks) and provides clearer confidence signals.

Yes. Typical speedups are ~1,000,000x faster than DFT on most tasks and 10,000-50,000x faster than conventional tools in adjacent domains. DFT remains appropriate for final-stage validation on shortlisted candidates.

See our Benchmarks page for quantitative comparisons. Generally: accuracy suitable for screening prioritization, with clear confidence indicators when predictions are less reliable.

No. Start with modules that match your workflow. Most pilots use 1-2 modules.

FluxMateria derives molecular and material properties from fundamental physics — specifically, from molecular geometry and physical constants. There are no trained models, no fitted parameters, and no statistical correlations. Every prediction is a deterministic calculation from first principles, which means the same input always produces the same output, and every result can be traced back to the underlying physics.

Molecules: Any organic molecule expressible as a SMILES string or SDF file. The ADMET panel covers standard drug-like chemical space (MW < 1000, organic elements). Novel scaffolds are supported but may receive lower confidence scores.

Materials: 73 elements (H through Bi, excluding lanthanides). Binary and ternary compounds are best supported. The materials engine covers 14 categories including III-V, II-VI, oxides, perovskites, chalcopyrites, and more. See Materials for the full list.

We document limitations honestly:

• Novel scaffolds far from validated chemical space may have lower confidence
• Some toxicity endpoints have limited experimental validation data
• Quaternary+ material compositions may have lower accuracy
• Lanthanide elements are not yet supported
• Predictions are for screening prioritization, not regulatory submission

Each module page includes a "Scope notes" section with specific limitations. See Benchmarks for quantitative accuracy data.

FluxMateria is designed for screening and prioritization, not direct regulatory submission. Results can inform which candidates to advance to experimental validation, but regulatory filings should rely on experimental data. We provide full provenance and methodology documentation to support internal decision-making and audit trails.

SMILES strings, SDF files, batch CSV/JSON, and material compositions (module-dependent). See Docs → Inputs.

High = reliable for this chemical space. Medium = informative but verify for critical decisions. Low = outside reliable prediction space. See individual module docs for endpoint-specific guidance.

Yes. Export formats include CSV, JSON, SDF, and PDF. API access is available for programmatic integration. See Integrations.

A low-confidence prediction means the molecule or material is outside the region where FluxMateria's physics model is most reliable. This does not mean the prediction is wrong — it means you should treat it as a hypothesis rather than a screening decision. Recommended actions:

• Do not use low-confidence results as pass/fail gates
• Flag the compound for experimental validation
• Check which specific endpoints are low-confidence (others may still be reliable)
• Consider whether the molecule has unusual structural features (macrocycles, metallocenes, etc.) that may be outside current coverage

See Troubleshooting for more guidance.

Input: SMILES strings, SDF/MOL files, batch CSV (one SMILES per row), JSON, and material compositions (element symbols + stoichiometry).

Output: CSV, JSON, SDF with annotations, and PDF decision packets with full provenance. All exports include confidence indicators and methodology metadata. See Getting Started for format details.

The interactive ADMET demo requires no account — input a SMILES and see results immediately. For batch evaluation and full platform access, contact us to schedule a pilot discussion.

Three video walkthroughs (Inverse Search, Materials, Workspace) plus an interactive batch ADMET demo where you can upload your own molecules. See Demos.

Discovery call → pilot proposal → onboarding → evaluation → documented outcomes. Most pilots run 4-8 weeks. See Pricing for details.

Pricing depends on modules and scope. We'll provide a specific proposal after understanding your workflow.

Then you've learned quickly. We define success metrics upfront so outcomes are unambiguous.

No. FluxMateria is physics-only and does not use customer data for ML/AI training, and data is not shared between customers. See Security.

We are early-stage; a SOC 2 roadmap is available on request. We're happy to discuss your specific compliance requirements during pilot conversations.