Search for: All records

Density-functional theory based molecular-dynamics simulations were used to investigate high-pressure chemical reactions in liquid mixtures of CO2 with several elements (Si, Mn, and Fe) at high temperatures of 2000–3000 K. Our ab initio simulations indicate that these reactant elements can reduce CO2 to C at high pressures (20 GPa) leading to the formation of C-C chains, with Si by far the most effective carbon-reducing agent. A combined chemical analysis using Bader charge analysis and crystal orbital Hamilton population (COHP) on simulation snapshots shows that significant charge transfer from the reducing element to the C atoms creates instability in the C-O covalent bonds. COHP analysis further shows that Mn/Fe-O and Mn/Fe-C bonding interactions are weaker compared to the Si counterparts. These results further our understanding of the redox chemistry of CO2 at conditions relevant to planetary mantle interiors and demonstrate the effectiveness of high pressure in the reduction of CO2 directly to solid carbon. 

X-ray spectroscopy has long been a powerful diagnostic tool for hot, dilute plasmas, providing insights into plasma conditions by measuring line shifts and broadenings of atomic transitions. The technique critically depends on the accuracy of atomic physics models used to interpret spectroscopic measurements for inferring plasma properties such as free-electron density and temperature. Over the past decades, the atomic and plasma physics communities have developed robust atomic physics models to account for various processes in hot, dilute classical plasmas. While these models have been successful in that regime, their applicability becomes uncertain when interpreting x-ray spectroscopy experiments of above-solid-density plasmas. Given that finite-temperature density-functional theory (DFT) offers a more accurate description of dense plasma environments, we present the development of a DFT-based multi-band kinetic model, VERITAS, designed to improve the interpretation of x-ray spectroscopic measurements in high-density plasmas produced by laser-driven spherical implosions. This work details the VERITAS model and its application to both time-integrated and time-resolved x-ray spectra from implosion experiments on OMEGA. The advantages and limitations of the VERITAS model will also be discussed, along with potential directions for advancing x-ray spectroscopy of dense and superdense plasmas. 

Abstract In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks. 

Organometal halide perovskites are promising materials for optoelectronic applications, whose commercial realization depends critically on their stability under multiple environmental factors. 

The ionic structure of high-pressure, high-temperature fluids is a challenging theoretical problem with applications to planetary interiors and fusion capsules. Here we report a multimessenger platform using velocimetry and angularly and spectrally resolved x-ray scattering to measure the thermodynamic conditions and ion structure factor of materials at extreme pressures. We document the pressure, density, and temperature of shocked silicon near $100\mathrm{GPa}$with uncertainties of 6%, 2%, and 20%, respectively. The measurements are sufficient to distinguish between and rule out some ion screening models. Published by the American Physical Society2024 

We describe an experimental protocol for the creation of a three- dimensional topological defect, a skyrmion, in a pseudo-spin-1/2 Bose-Einstein condensate (BEC) confined in a spin-independent har- monic trap. We show that one can imprint the skyrmion on the BEC within a few tens of microseconds using a Raman process with the structured laser fields. We numerically solved the mean- field Gross-Pitaevskii equation to examine our imprinting scheme, and found that all parameters we use are experimentally feasible.