TY - JOUR
T1 - A Framework for Validating ML Potentials against Experimental Phonon Dispersion and Elastic Constants
AU - Sophie Laurent
AU - Pierre Dubois
AU - Marc Lefevre
AU - Claire Moreau
JF - Journal of Computational and Data-Driven Materials Engineering
JO - J. Comput. Data-Driven Mater. Eng.
SN - 3149-9368
Y1 - 2026
VL - 5
IS - 1
DO - 10.68159/z008468123
SP - 66
N2 - Machine learning interatomic potentials (MLIPs) are widely validated against density functional theory (DFT), yet this practice risks reproducing DFT’s systematic biases rather than true experimental behavior. We present a concise four-level hierarchical framework for rigorous experimental validation focused on phonon dispersion and elastic constants. Level 1 evaluates static room-temperature elastic constants; Level 2 assesses zone-center phonon frequencies (Raman/IR); Level 3 examines full phonon dispersion curves from inelastic neutron/X-ray scattering; and Level 4 tests temperature-dependent shifts and softening. For each level we provide key observables, simple comparison metrics, uncertainty-aware criteria, and typical failure modes. A practical validation protocol and minimum reporting standards (Level 1 on ≥3 materials, Level 2 on ≥1) are proposed. This framework complements DFT benchmarks by anchoring MLIPs in experimental reality, improving reliability for thermal, mechanical, and vibrational applications in materials science.
UR - https://iamrp.net/z008468123
ER -