Solid-State Battery Simulation
We help SSB startups reach manufacturability faster. With custom Deep Operator Networks that simulate ion transport, stress-coupled kinetics, and fracture mechanics, we predict critical current density and stack pressure limits in milliseconds.
How it works
Stop burning cash on failed batches. We combine the reliability of high quality solvers (PyBaMM/FEniCS) with the speed of Neural Networks. We deliver a custom, pre-trained Digital Simulator calibrated to your specific chemistry.
Speed & Accuracy
DFT is accurate but limited to nanoseconds. Our neural networks simulate the entire charge/discharge cycle and the degradation over hundreds of cycles in just hours. We figure out why an interface is failing under certain conditions, debug manufacturing variances, and act as a process control engine.
Smarter Batches
A single failed validation cycle costs 3 months and $100k+ in burn. We identify failure modes digitally in days.
Ultra Light Deployment
Ultra Light Deployment
Explainable AI
Regulators reject Black Boxes. We use Physics-Informed models that respect biological laws, making them auditable.
Fast & Accurate
DFT is accurate but limited to nanoseconds. Standard ML is fast but hallucinates physics. Our PINNs combine the best of both: speed capable of simulating thousands of cycles with physics-enforced constraints.
Data Efficiency
You don't need Google-sized datasets. Our PINNs use physical constraints to learn accurately from smaller, proprietary datasets.
Compatible with your Lab Stack
Our API connects directly to your existing Electronic Lab Notebooks and cloud data lakes, enhancing your current workflow with predictive intelligence.
