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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.

In the rapidly evolving field of computational and data-driven materials engineering, AI-guided design has emerged as a transformative paradigm, leveraging machine learning and high-throughput computations to accelerate materials discovery. However, persistent bottlenecks in experimental validation hinder the seamless transition from computational predictions to real-world applications. This conceptual manuscript examines these challenges through a systems-level lens, framing them within the broader materials informatics ecosystem. Key issues include the misalignment between simulation-derived datasets and experimental realities, uncertainty propagation in model inferences, and the inefficiencies in closed-loop discovery pipelines. We introduce the Validation Alignment Network (VAN) framework, an original conceptual architecture that integrates representation learning, uncertainty quantification, and simulation-experiment coupling to mitigate these bottlenecks. By emphasizing epistemic risk structures and computational steering logics, VAN provides interpretive insights into optimizing discovery workflows. Implications extend to enhancing autonomous discovery systems and foundation models for science, fostering more robust AI integration in materials research. This work underscores the need for infrastructure-level advancements to bridge computational predictions with empirical validation, ultimately advancing data-driven materials innovation.