WEBVTT

NOTE Replacement captions. A disputed theorem-count cue is excluded pending machine-generated reconciliation. Cues after the exclusion are retimed by -7.890 seconds.

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Water and air touch every human life.

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Yet water scarcity affects about two billion people, and outdoor air pollution is linked to millions of premature deaths each year.

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Both problems are materials-limited, and both are corrupted by the same predictable computational error.

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Universal machine-learning potentials learn tidy bulk crystals where every atom has many regular neighbors.

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But the membranes, sorbents, and catalysts that purify water and air function at pores, metal centers, and single-atom sites—environments with fewer neighbors.

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There, the energy surface is systematically softened by 15 to 60 percent.

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A softened barrier makes the wrong membrane look selective, the wrong sorbent look stable, or the wrong catalyst look fast.

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Across millions of candidates, tiny systematic errors flip rankings.

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Experiments chase false priorities while better materials stay on the bench.

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Corrected binding and barrier energies overturn raw rankings in desalination membranes, atmospheric-water sorbents, and lithium-selective frameworks.

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The same correction recovers activation energies for low-temperature exhaust catalysts, soot filters, and volatile-organic-compound oxidizers, separating real activity from artificial promise.

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The correction measures error as a field over local coordination, anchored to three observables and pinned to zero in bulk.

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Analytic forces are added at runtime, keeping molecular dynamics nearly as fast as the base model.

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Across 36 blind model-material pairs, corrected predictions correlate at 0.906 with zero fitted parameters.

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A membrane ranking is supported only when its local environments fall inside the measured domain, and a metastable sorbent phase is flagged honestly rather than sold as predicted.

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The failure mode is coordination-specific, not climate-specific.

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One measured correction-and-verification layer can raise discovery reliability across water, air, batteries, and direct air capture.

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The goal is predictions laboratories can trust.
