Batteries: Solve End-to-End Production Challenges
Battery R&D runs on two kinds of testing that usually live apart: analytical testing on materials, measured by PSD, ICP, XRD, or TGA, and electrochemical testing on cells. Because cycling can take weeks or months to return results, the context of what was tested and why gets lost in spreadsheets along the way, and teams are left guessing at how an upstream formulation or process change actually shows up downstream.
Uncountable puts both testing streams on one barcoded record, from raw material through cell assembly, formation, and cycling QC. For materials and advanced-materials teams, battery testing becomes just one more form of analytical testing; for cell manufacturers, it's the through-line from materials characterization to electrochemical performance, in one place, for every build. Structure-process-property relationships stay connected instead of scattering across systems, early-stage learnings carry through to multi-million-dollar scale-up instead of getting lost, and machine learning is trained on your own formulation, process, and cycling history, not a generic model.
See how it works on your own battery data by booking a personalized demo.
FAQs
This guide covers how Uncountable connects analytical testing (PSD, ICP, XRD, TGA) with battery testing so materials and process teams can see how a formulation or process change affects cell performance, and how a barcoded sample-tracking workflow carries traceability from raw material through cell assembly, formation, and cycling QC.
It's built for two groups working from opposite ends of the same problem: materials and advanced-materials teams running formulation and process work from bench-top to pilot-line, and cell manufacturers who need the through-line from materials characterization to electrochemical performance, in one place, for every build.
Analytical testing measures material properties directly, using methods like PSD, ICP, XRD, and TGA. Battery testing measures electrochemical performance through cycling, which can take weeks or months to return results. Uncountable tracks both under the same record, so when e-chem results come back, the testing context, what was changed and why, is still there to make them actionable.
Every sample is barcoded and tracked as it moves through the workflow: for example, a cathode active material becomes a slurry, then a pressed cathode, which is combined with an anode, electrolyte, and separator into a cell assembly, then carried through formation and cycling QC. Nothing is logged by hand, and the full genealogy of a cell stays on one connected record.
Models are trained on your own formulation, process, and cycling data to help rank the next builds worth running, not a generic model. Structure comes first: because the underlying data is connected and structured, the machine learning built on top of it is grounded in your actual R&D history.
