Question 1

What does Scale Prognostics actually predict?

Accepted Answer

Capacity retention vs. cycle number for silicon-graphite lithium-ion cells, plus per-cycle decomposition into degradation mechanisms (LLI, LAM-Si, LAM-Gr, transport-driven failure). The engine simulates 1,000 cycles in ~30 ms.

Question 2

How accurate is the model?

Accepted Answer

Calibrated against 8 published silicon-anode datasets — 6 at A+ (RMSE 0.13–0.32 percentage points) and 2 at A on knee-prone cells (RMSE 1.01–1.23 pp). Mean RMSE 0.44 pp. Held-out forward-prediction RMSE on knee-prone cells is materially higher (mean 11.86 pp at 70% retention cutoff) and is the focus of ongoing architecture-sprint work; see /methodology for the full breakdown. Validation spans 2.5–20% silicon, 0.5C to 2C charge rates, and 25°C to 45°C temperatures.

Question 3

How does this compare to PyBaMM or running my own model?

Accepted Answer

PyBaMM solves full PDE (Doyle-Fuller-Newman) systems — rigorous but ~1000x slower and requires extensive parameterization. Our engine runs 1,000 cycles in ~30ms with 7 coupled aging mechanisms, built-in uncertainty quantification, and auto-calibration. Use PyBaMM for voltage-level predictions; use us for lifetime predictions at scale.

Question 4

What chemistries are supported?

Accepted Answer

Silicon-graphite anodes with NMC cathodes, with silicon content from 2.5% up to 20%. Pure-graphite cells work but the competitive advantage is highest for silicon-containing cells. LFP cathode support is on the roadmap.

Question 5

What does it cost?

Accepted Answer

Free trial: 50 simulations/month, no credit card. Starter: $499/month for 2,500 simulations. Professional: $1,499/month for 10,000. Enterprise pricing on request.

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