Neutron spectroscopy is a premier tool for probing atomic dynamics, yet extracting physical insights from experimental data remains a significant computational challenge. Traditional methods—Empirical Force Fields and Density Functional Theory (DFT)—frequently fail to balance the necessary accuracy with the required computational scale. Our group is developing a high-performance computational framework to bridge this gap. By leveraging material simulation and Machine Learning Interatomic Potentials (MLIPs), we aim to accelerate the interpretation of inelastic (INS) and quasi-elastic neutron scattering (QENS) data, enabling the study of larger, more complex material systems than ever before.

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