ArticleThe journal of physical chemistry letters2026
High-Pressure Inelastic Neutron Spectroscopy: Experimental Validation of Machine-Learned Interatomic Potential Energy Landscapes.
Article in The journal of physical chemistry letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Machine-learned interatomic potentials (MLIPs) promise near density-functional theory accuracy at a fraction of the computational cost, offering a route toward predictive atomistic modeling of molecular and condensed-phase materials. Yet their reliability beyond the training regime remains difficult to establish experimentally. Here we use pressure-dependent broadband inelastic neutron spectroscopy (INS) as an experimental probe of MLIP transferability. Using a low-background NiCrAl high-pressure clamp cell, we measure INS spectra of crystalline 2,5-diiodothiophene at 10 K under atmospheric pressure and at 1.5 GPa. MACE-based MLIPs, fine-tuned on targeted DFT data, reproduce the experimental spectra across 0-1200 cm
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