High-Pressure Inelastic Neutron Spectroscopy: A true test of Machine-Learned Interatomic Potential energy landscapes

TL;DR

This study demonstrates that machine-learned interatomic potentials can accurately predict vibrational spectra of crystalline molecules under high pressure, validated through inelastic neutron spectroscopy, marking a significant step in testing their transferability.

Contribution

It provides the first experimental validation of MLIP transferability across different thermodynamic states using high-pressure INS, confirming their predictive accuracy beyond training conditions.

Findings

MLIP accurately reproduces experimental spectra under pressure

Model captures pressure-induced spectral shifts and intermolecular effects

High-pressure INS serves as a rigorous benchmark for MLIPs

Abstract

Machine-learned interatomic potentials (MLIPs) promise to provide near density-functional theory accuracy at a fraction of the computational cost, offering a transformative route toward genuinely predictive chemistry. Yet their predictive validity beyond the training regime remains largely untested experimentally. Here we use pressure-dependent broadband inelastic neutron spectroscopy (INS) as a direct experimental probe of MLIP transferability. Employing a newly developed high-pressure superalloy clamp cell, we measure INS spectra of crystalline 2,5-diiodothiophene at 10~K under ambient conditions and at 1.5~GPa. A MACE-based MLIP, fine-tuned on targeted DFT data, reproduces the experimental spectra across 0--1200~cm$^{-1}$ at both pressures and remains thermodynamically stable under rigorous molecular dynamics validation at 300~K. The model captures systematic pressure-induced blue shifts arising from steric stiffening and reproduces an anomalous red shift at 453~cm$^{-1}$ driven by pressure-modified intermolecular interactions, providing direct validation of its many-body character. This constitutes the first experimental demonstration of MLIP transferability across distinct thermodynamic states using neutron spectroscopy, and establishes high-pressure INS as a stringent benchmark for predictive machine-learned potentials.