Skip to main content
Search

# Machine-learned interatomic potentials

3 related articles

Subscribe to updates
  1. arXiv

    Linear ACE potentials get calibrated uncertainty: a delete-one-configuration jackknife sets the shape and one conformal quantile the scale, putting most of 20 silicon properties inside 95% intervals

    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.

  2. arXiv

    Excited-state machine-learned potentials get a local Hamiltonian: two molecules 10 Å apart keep exactly the union of their levels

    Excited-state energies, unlike ground-state energies, are not sums of atomic contributions, so multistate readouts that pool atomic terms drift when molecules grow larger or two molecules appear together. LEH-MACE instead assembles a symmetric matrix from one diagonal block per atom and coupling blocks for atom pairs, and takes its eigenvalues as the singlet excitation energies, so the spectrum of two molecules beyond the cutoff is exactly the union of their spectra. On TD-DFT data for about 303,000 geometries of 106,933 molecules, the advantage grew away from the training distribution, and only this construction kept single-molecule accuracy for molecule pairs.

  3. Nanoscale

    Hydrogen sulphide on refractory oxides: a foundation model screens configurations while DFT spectra reproduce the experimental band order

    Whether hydrogen sulphide physisorbs or dissociates on nanoscale refractory oxide surfaces had been hard to settle because the configuration space is too large to enumerate. This work pairs unbiased automated adsorption sampling with an off-the-shelf atomistic foundation model: MACE's MPA screens candidate minima, each survivor is re-optimised with PBE-D2*(N) and HSE06 DFT, and DFT multiconfigurational infrared spectra are compared with in situ spectra at 150 K. The computed spectra reproduce the red-shift order of the H–S stretch across the three materials: silica at 2603 cm⁻¹, forsterite at 2588 cm⁻¹ and MgO at 2504 cm⁻¹.

You’re all caught up