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A Framework for Validating ML Potentials against Experimental Phonon Dispersion and Elastic Constants
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.
Journal of Computational and Data-Driven Materials Engineering
Original Research | Open access | 18 January 2026 | Article: 66
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