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Abstract:

The robust diagnosis of conditions generated in warm dense matter experiments remains a persistent challenge. Here, we describe the measurement of shock-compressed aluminium at 50 GPa with angle-resolved femtosecond x-ray Thomson scattering (XRTS) over a wide range of scattering wave vectors at the European X-Ray Free-Electron Laser. The measured plasmon dispersion and line shape show that the standard approach for analyzing XRTS spectra, using uniform-electron-gas models, systematically overestimates the resonance energy by up to 8 eV. We present an approach using methods that agrees within the experimental uncertainty and demonstrates how accounting for shock-induced disorder in shock-compressed systems is critical for their understanding, providing evidence that treatments are required for reliable XRTS inference in warm dense aluminium.

Abstract:

Abstract Machine learning (ML) density functional approximations (DFAs) have seen a lot of interest in recent years, often being touted as the replacement for well-established non-empirical DFAs, which still dominate the field. Although highly accurate, ML-DFAs typically rely on large amounts of data, are computationally expensive, and fail to generalize beyond their training domain. In this work we show that differentiable DFT with Kohn–Sham regularization can be used to accurately capture the behavior of known local density approximations from small sets of synthetic data without using localized density information. At the same time our analysis shows a strong dependence of the learning on both the amount and type of data as well as on model initialization. By enabling accurate learning from sparse energy data, this approach paves the way towards the development of custom ML-DFAs trained directly on limited experimental or high-level quantum chemistry datasets.