A predicted crystal is not a material.
A synthesized powder is not a cell.
A cell is not a fleet, a filter, a fertilizer plant, or a solar farm.
Between a computational screen and a commercial product stands a chain of handoffs — and most predictions never survive it.
Millions of predicted crystals shrink to a few hundred independently synthesized.
The conventional answer is a bigger generator.
But volume without trust only amplifies the same handoff failure.
What is missing is a correction and verification layer that makes predictions trustworthy before they reach the lab.
Consider cobalt-free lithium-manganese-rich cathodes.
A corrected ranking sifts more than a million compositions, then passes the best candidates through synthesis, atomic-layer coating, pouch-cell fabrication, and finally into an automaker's prismatic cell line.
Each handoff carries a bounded error budget, or it breaks.
The correction measures error as a field over local coordination, anchored to measured observables and pinned to zero in bulk.
Analytic forces apply it at runtime, so simulations stay fast.
Across 36 blind model-material pairs, corrected surface energies correlate at zero point nine zero six, with zero fitted parameters.
The verification layer separates supported predictions from unsupported ones with machine-checked proofs.
It can flag impossibility, not just uncertainty, before synthesis starts.
Experimental budgets are finite; false positives are expensive.
The layer makes sure the work goes to candidates that actually deserve it.
The same logic reaches solid-state electrolytes, direct-air-capture frameworks, ammonia catalysts, and lead-free perovskites.
Different markets, different partners, same geometry of wrongness.
In every chain, the scarce resource is not prediction volume.
It is the trust that turns a predicted crystal into a commercial cell.