Physics-constrained AI for operando XRD analysis
SynchNova transforms time-resolved X-ray diffraction data into actionable crystallographic intelligence — automatically, in real time, with full physics traceability.
- 3
- Analysis modes, from screening to refinement
- 30+
- Years of synchrotron X-ray experience
- 2
- U.S. patents and pending applications
One platform. Three analysis modes.
From real-time AI screening to high-accuracy whole-pattern analysis — the platform adapts to your throughput and precision requirements.
Fast — AI-powered screening
Physics-informed deep learning delivers phase and structural parameter estimates directly from raw diffraction patterns. Designed for high-throughput environments and real-time process monitoring.
Screening pass ahead of refinement
Standard — physics-constrained optimization
Whole-pattern optimization enforces crystallographic physics at every step, ensuring outputs are not just statistically fit but physically valid. Balances speed and accuracy for routine operando analysis.
Seconds per frame
High-accuracy — full whole-pattern refinement
Proprietary refinement engine delivers publication-quality crystallographic parameters. Fully automated from raw data to final result, with built-in quality diagnostics at every stage.
Per-frame fit-quality diagnostics on every result
What the analysis delivers
Every result carries its own quality diagnostics, so no number is reported without the evidence for trusting it. Validation data and performance figures are available on request under a confidentiality agreement.
Phase transition detection
Automated identification of structural phase transitions, secondary phase formation and degradation-related crystallographic changes across full charge and discharge cycles.
Lattice parameter tracking
High-precision structural parameter evolution extracted frame by frame, with automated artifact rejection and quality validation.
State-of-health scoring
Crystallographic health indicators — phase reversibility, structural breathing and fit quality trends — computed and graded automatically without manual review.
Multi-source instrument support
Configurable for laboratory and synchrotron X-ray sources. Variable angular ranges and step sizes are accommodated through physics-based data normalization.
Where the platform deploys
From factory floor quality control to advanced materials discovery — the same analytical core, configured for your context.
Battery electrode quality control
Real-time crystallographic quality control for lithium-ion electrode manufacturing lines. Detect structural defects and phase anomalies before cells are assembled, reducing scrap, improving yield and building a data record of crystallographic quality at scale.
XRF–XRD fusion for rare-earth characterization
Quantitative rare-earth element characterization from unconventional feedstocks — coal fly ash, mine tailings and mixed mineral concentrates. A proprietary physics-based fusion approach operates without empirical calibration libraries, enabling analysis where conventional methods fail.
Operando crystallography at scale
Automated analysis of time-resolved diffraction sequences lets research groups extract complete crystallographic time series from datasets of hundreds to thousands of frames, without manual intervention or expert post-processing.
Garnet and argyrodite characterization
Phase quantification of LLZO and argyrodite solid electrolyte systems, including polymorph discrimination and phase purity assessment — key quality indicators for solid-state battery cell manufacturing.
Physics first. Machine learning second.
Every result is grounded in crystallographic physics. AI accelerates the analysis; the physics engine validates every output.
Data ingestion and quality assessment
Accepts all major XRD file formats. Automated diagnostics flag anomalies and assess signal integrity before analysis begins.
Automated instrument calibration
Systematic measurement artifacts are identified and corrected frame by frame, without manual intervention or external calibration standards.
AI-assisted phase analysis
A proprietary deep learning model rapidly identifies crystallographic phases and structural parameters, providing reliable starting conditions for the refinement engine.
Physics-constrained refinement
Proprietary whole-pattern refinement enforces crystallographic physics at every step. Phase fractions, lattice parameters and microstructure are physically valid, not just statistically fit.
Automated reporting and export
Publication-quality figures, interactive reports and multiple export formats, with full data provenance from raw measurement to final result.
Chemistry-agnostic
Any battery material system — cathode, anode or electrolyte — is supported without custom engineering, so new chemistries can be deployed as the technology landscape evolves.
Instrument-independent
Supports laboratory and synchrotron X-ray sources at any wavelength without manual reconfiguration.
Electrochemistry-integrated
Optional voltage and current data brings electrochemical context into the structural analysis, improving accuracy and enabling richer diagnostics.
Intellectual property
Core platform innovations are covered by a pending U.S. patent application on the hybrid AI and physics-based approach to automated X-ray diffraction analysis.