James R. Kermode

Linear atomic cluster expansion (ACE) potentials are misspecified when trained on deterministic density-functional labels, so Bayesian posterior variances understate their errors. A delete-one-configuration CR3 jackknife covariance supplies the shape and one conformal quantile from held-out data supplies the scale, bringing per-atom force regions close to nominal coverage on held-out silicon and propagating the same parameter distribution through implicit differentiation to 21 silicon properties; of 20 scored properties, 17 fall inside 95% intervals for the smaller model and 15 for the larger, whose intervals are about 4 times tighter.

Linear atomic cluster expansion (ACE) potentials are misspecified when trained on deterministic density-functional labels, so Bayesian posterior variances understate their errors. A delete-one-configuration CR3 jackknife covariance supplies the shape and one conformal quantile from held-out data supplies the scale, bringing per-atom force regions close to nominal coverage on held-out silicon and propagating the same parameter distribution through implicit differentiation to 21 silicon properties; of 20 scored properties, 17 fall inside 95% intervals for the smaller model and 15 for the larger, whose intervals are about 4 times tighter.