Okay, so AI predicted three hundred eighty thousand stable new materials.
Huge, right?
But by late 2023, only seven hundred thirty-six had actually been synthesized.
That’s zero point two percent.
And no, this isn’t just labs moving slowly.
A crystal can look perfect on a computer and still have no practical chemical path into existence.
Prediction is scaling.
Synthesizability isn’t.
Most screens skip four brutal filters.
One: can real precursors reach the target instead of some competing phase?
Two: are the forces accurate away from a tidy, equilibrium crystal?
Three: what happens when the material has vacancies, disorder, or stacking faults—like real materials do?
And four: what does failure cost?
Usually weeks of lab time and thousands of dollars.
This is not a tiny accuracy problem.
Miss an ion-migration barrier by one hundred milli-electronvolts, and the predicted hopping rate changes about fifty-fold at room temperature.
You can literally call a conductor an insulator.
The answer isn’t a bigger generator.
It’s a correction and verification layer: an environment error field that measures where universal potentials go wrong, then corrects them at runtime with proper forces.
Better candidates rise.
Bad ones get flagged before the furnace turns on.
That matters because batteries are tied directly to roughly twenty percent of the emissions cuts needed by 2030.
Cobalt-free cathodes could also break a supply chokepoint: about seventy percent of cobalt comes from the Democratic Republic of Congo.
And every decade of delay locks in emissions we cannot recover later.
The goal is simple: predictions that flow to synthesis, characterization, and scale—not another generation cycle.
Read the full article to see how the zero-point-two-percent gap closes.