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🧬 Digital DNA™⚗️ Material Genomics
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🧬 Platform Deep Dive

The Digital DNA™
Platform

Three vertically integrated layers of intelligence — from molecular material fingerprinting to real-time predictive AI — built exclusively for electrochemical systems.

Architecture

Three Intelligence Layers

Click each layer to expand. Every layer feeds the next — creating compounding intelligence no single-layer platform can match.

LAYER 01
🧬
Material Genomics
Deep chemical, structural and electrochemical fingerprints for every critical battery material — the reference library that powers all downstream intelligence.
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Molecular FingerprintingXRD, XPS, EIS and SEM signatures captured for every material batch — creating an unmatched genomic reference library spanning 12+ years and 50M+ data points.
12+ Year Reference LibraryNMC, LFP, NCA, LMO and emerging chemistries — the largest genomic battery materials database ever assembled, continuously updated.
Cross-Chemistry BenchmarkingEvery new material batch compared against the reference library in real time — deviations flagged before they enter production and propagate downstream.
Root-Cause MappingField failure modes traced back to specific material traits — closing the loop between factory quality decisions and in-service performance outcomes.
LAYER 02
🔗
Genomic Thread™
A continuous, searchable data spine connecting mine → material → electrode → cell → pack → field. No gaps, no siloes, no hand-off loss.
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End-to-End Data LineageA single continuous record linking every supply-chain layer into one coherent, queryable dataset — enabling traceability at any point in time.
Blockchain ProvenanceImmutable checkpoints at critical supply-chain handoffs for regulatory compliance, ESG reporting, and audit readiness under the EU Battery Regulation.
Real-Time Streaming PipelineMES, BMS, SCADA and field telemetry ingested continuously — keeping the Genomic Thread current as production and operations evolve in real time.
Root-Cause AI EngineWhen field anomalies appear, the Thread traces them backwards through every manufacturing decision in seconds — identifying the exact batch and process step responsible.
LAYER 03
🤖
Battery Genomic Intelligence
Physics-informed ML trained on genomic fingerprints — not anonymous fleet averages. Chemistry-specific predictions for safety, lifetime and performance.
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Physics-Informed ML (PINN)Degradation models constrained by electrochemical physics — not black-box curve fitting. Chemistry-specific predictions with quantified uncertainty bounds.
Fire Precursor DetectionThermal runaway risk identified weeks ahead by correlating subtle BMS signals with material genomic traits — before any conventional alarm would trigger.
Duty-Cycle OptimisationPersonalised charge/discharge protocols derived from each cell's genomic profile — extending usable lifetime by up to 3× versus generic BMS strategies.
Federated Fleet LearningModels improve continuously from every connected battery without sharing raw data — privacy-preserving collective intelligence at global scale.
Digital Twin

A Living Mirror of Every Battery

Every battery pack under Digital DNA™ management has a continuously updated digital twin — a precise virtual replica reflecting its genomic heritage, operational history, and current electrochemical state.

3×
Battery Lifetime Extension

Genomic-aware duty-cycle optimisation extends usable battery life by up to 3× versus generic BMS strategies — a direct TCO reduction.

99%
Prediction Accuracy

Chemistry-specific PINN models achieve state-of-health prediction accuracy validated against independent field datasets.

Wks
Fire Warning Lead Time

Thermal runaway precursors detectable weeks before failure via genomic signal correlation.

40%
Scrap Rate Reduction

Genomic quality control at material intake catches deviations before they enter production.

See Digital DNA™ in Action

Schedule a tailored technical demonstration for your team. We'll show you exactly how the three intelligence layers apply to your specific use case.

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