Automated Crystallographic Intelligence
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.
11.90% Rwp
Verified mean fit quality
203 frames
Industrial full-cell dataset
3 modes
Fast · Standard · High-Accuracy
12+
Built-in material systems
Platform
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.
01 / Fast Mode
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.
Throughput: milliseconds / frame
02 / Standard Mode
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.
Throughput: seconds / frame
03 / High-Accuracy Mode
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.
Verified: 11.90% mean Rwp on industrial full-cell data
Verified Performance
Results on real industrial data
Performance figures are validated on commercial-grade battery electrode measurements, not synthetic benchmarks.
11.90%
Mean Rwp
Whole-pattern fit quality on NMC/Graphite full-cell operando dataset
203
Frames analyzed
Complete charge/discharge cycling sequence
Sub-Å
Lattice precision
Sufficient to detect phase transitions critical to battery degradation
<2%
False positive rate
Go/No-Go criterion validated on industrial cell data
The platform resolves crystallographic signatures that matter for battery health and manufacturing quality — automatically, in the same analysis pass.
Phase transition detection
Automated identification of structural phase transitions, secondary phase formation, and degradation-related crystallographic changes across full charge/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 accommodated through physics-based data normalization.
Applications
Where the platform deploys
From factory floor quality control to advanced materials discovery — the same analytical core, configured for your context.
Manufacturing
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.
Critical Minerals
XRF-XRD Fusion for REE Characterization
Quantitative rare-earth element characterization from unconventional feedstocks — coal fly ash, mine tailings, and mixed mineral concentrates. Proprietary physics-based fusion approach operates without empirical calibration libraries, enabling analysis where conventional methods fail.
Research
Operando Crystallography at Scale
Automated analysis of time-resolved diffraction sequences enables research groups to extract complete crystallographic time series from datasets of hundreds to thousands of frames — without manual intervention or expert post-processing.
Solid-State Electrolytes
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.
Technology Approach
Physics first.
Machine learning second.
Every result is grounded in crystallographic physics. AI accelerates analysis — the physics engine validates every output.
01
Data ingestion and quality assessment
Accepts all major XRD file formats. Automated data quality diagnostics flag anomalies and assess signal integrity before analysis begins.
02
Automated instrument calibration
Systematic measurement artifacts are identified and corrected frame by frame — without manual intervention or external calibration standards.
03
AI-assisted phase analysis
Proprietary deep learning model rapidly identifies crystallographic phases and structural parameters, providing reliable starting conditions for the refinement engine.
04
Physics-constrained refinement
Proprietary whole-pattern refinement enforces crystallographic physics at every step. Outputs — phase fractions, lattice parameters, microstructure — are physically valid, not just statistically fit.
05
Automated reporting and export
Publication-quality figures, interactive reports, and multiple export formats. Full data provenance from raw measurement to final result.
Chemistry-agnostic
Any battery material system — cathode, anode, or electrolyte — is supported without custom engineering. New chemistries can be deployed rapidly as the technology landscape evolves.
Instrument-independent
Supports laboratory and synchrotron X-ray sources at any wavelength without manual reconfiguration — no instrument-specific setup required.
Electrochemistry-integrated
Optional integration of voltage and current data brings electrochemical context into the structural analysis — improving accuracy and enabling richer diagnostics.
Continuously improving
Proprietary learning mechanisms allow the platform to adapt to new material systems with minimal data — so accuracy improves the more the platform is used.
Funding & Recognition
Federal R&D investment
SynchNova is advancing its platform through federal SBIR programs, validating the technology against the most demanding real-world applications.
U.S. Department of Energy · SBIR
AI-Powered XRD Quality Control for Battery Electrode Manufacturing
Developing inline crystallographic quality control for lithium-ion electrode production lines, targeting the U.S. battery manufacturing sector and the growing domestic gigafactory ecosystem.
National Science Foundation · SBIR
Physics-Constrained Fusion for Critical Minerals Characterization
Developing quantitative mineral analysis for unconventional feedstocks using proprietary physics-based fusion of X-ray techniques — enabling characterization where conventional approaches cannot.
Intellectual Property
Patent Pending
Core platform innovations are protected under a pending U.S. patent application covering the hybrid AI and physics-based approach to automated X-ray diffraction analysis.