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Nielsen, Michael H.

Publications and source records attributed to Nielsen, Michael H..

Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. Here, we discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

36 MATERIALS SCIENCE↗

Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon

Improved understanding of structural and chemical properties through local experimental probes, such as X-ray absorption near-edge structure (XANES) spectroscopy, is crucial for the understanding and design of functional materials. In recent years, significant advancements have been made in the development of data science approaches for the automated interpretation of XANES structure–spectrum relationships. However, existing studies have primarily focused on crystalline solids and small molecules, while fewer efforts have been devoted to disordered systems. Thus, in this work, we demonstrate the development of neural network models for predicting and interpreting XANES spectra of amorphous carbon (a-C) from local structural descriptors. Comparison between different structural descriptors expectedly shows that the inclusion of both bond length and bond angle information is necessary for an accurate prediction of the spectra. Among the descriptors considered in this work, we find that the local many-body tensor representation yields the highest accuracy and greatest interpretability so that it can be leveraged to understand the importance of structural motifs in determining XANES spectra. Furthermore, we also discuss performance of neural network models for predicting both local structure features, such as bond lengths and bond angles, and global chemical composition, such as the sp:sp 2 :sp 3 ratio.

36 MATERIALS SCIENCE↗

Investigating the electronic structure of high explosives with X-ray Raman spectroscopy

Abstract We investigate the sensitivity and potential of a synergistic experiment-theory X-ray Raman spectroscopy (XRS) methodology on revealing and following the static and dynamic electronic structure of high explosive molecular materials. We show that advanced ab-initio theoretical calculations accounting for the core-hole effect based on the Bethe-Salpeter Equation (BSE) approximation are critical for accurately predicting the shape and the energy position of the spectral features of C and N core-level spectra. Moreover, the incident X-ray dose typical XRS experiments require can induce, in certain unstable structures, a prominent radiation damage at room temperature. Upon developing a compatible cryostat module for enabling cryogenic temperatures ( $$\approx$$ ≈ 10 K) we suppress the radiation damage and enable the acquisition of reliable experimental spectra in excellent agreement with the theory. Overall, we demonstrate the high sensitivity of the recently available state-of-the-art X-ray Raman spectroscopy capabilities in characterizing the electronic structure of high explosives. At the same time, the high accuracy of the theoretical approach may enable reliable identification of intermediate structures upon rapid chemical decomposition during detonation. Considering the increasing availability of X-ray free-electron lasers, such a combined experiment-theory approach paves the way for time-resolved dynamic studies of high explosives under detonation conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The role of detonation condensates on the performance of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) detonation

Thermochemical models of detonation are widely used to estimate energy delivery, but they are based on the assumption that the carbon-rich condensates (soot) formed during detonation are very similar to bulk carbon. Here, we present an analytic equation of state (EOS) based on experimental detonation data for soot formed during the detonation of triaminotrinitrobenzene (TATB)-based high explosives. X-ray photoelectron spectra of several detonation soots are used to determine the elemental nitrogen abundance, with surprisingly high values for TATB. The proposed TATB soot EOS is highly compressible at low pressures and shares some features of glassy carbon, exhibiting graphite- and diamond-like behavior as a function of pressure. We demonstrate the influence of formed soot on detonation performance, including a lowering of the detonation velocity at typical charge densities, and a more compressive product Hugoniot at overdriven conditions. The soot model improves the accuracy of thermochemical calculations for TATB-based explosives across a wide range of states. Detonation velocity predictions for HMX (cyclotetramethylene-tetranitramine)-TATB blends with 80% or more TATB content, as well predictions for 1,3-diamino-2,4,6-trinitrobenzene (DATB) and 3-nitro-1,2,4-triazol-5-one (NTO), which share some features with TATB, are also improved.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Raman signatures of detonation soot

Soot generated from the detonation of nine explosives was investigated with Raman microspectroscopy and high-resolution transmission electron microscopy (HRTEM). The first order bands, located at approximately 1,580 (G), 1,350 (D1), 1,620 cm –1 (D2), 1,500 (D3), and 1,200 (D4), are used to provide information on the carbon allotropes and defects. The intensity ratios and the full-width at half-maximum provide an indication of the disorder in the material. The Raman spectra of soot acquired from the detonation of nine explosives showed differences which can be used to identify the precursor explosive. Overall, most differences in the spectra were correlated with the presence of amorphous carbon in the soot. In additional to identification of distinct bands in the spectra via deconvolution, principal component analysis was also used to differentiate the different types of soot. HRTEM performed on soot samples with different explosive precursors identified several distinct allotropes of carbon such as nanodiamonds, carbon onions, graphite fibers, and amorphous carbon.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Detonation-induced transformation of graphite to hexagonal diamond

In this work, we explore the structural evolution of highly oriented pyrolytic graphite (HOPG) under detonation-induced shock conditions using in situ synchrotron x-ray diffraction in the ns timescale. We observe the formation of hexagonal diamond (lonsdaleite) at pressures above 50 GPa, in qualitative agreement with recent gas gun experiments. First-principles density functional calculations reveal that under uniaxial compression, the energy barrier for the transition toward hexagonal diamond is lower than that for cubic diamond. Finally, no indication of cubic diamond formation was observed up to >70 GPa.

36 MATERIALS SCIENCE↗

Observation of Variations in Condensed Carbon Morphology Dependent on Composition B Detonation Conditions

Carbon particulates generated during detonation depend upon high explosive type, composition, and detonation conditions. Although explosive composition greatly affects particulates, the focus of this work is on how detonation geometries that induce much higher temperatures and pressures in the high explosive lead to differing particulate morphologies. In this study, two geometries were used: Detonations were initiated in Composition B cylinders at one end in conventional detonations and initiated at both ends to produce colliding detonations. Each of these detonations was observed on the sub-μs timescale using fast radiography capturing images of the front moving through the cylinder, and colliding detonation fronts in real-time. These imaging experiments were complemented with time-resolved small-angle x-ray scattering (SAXS) experiments that were able to observe and determine the varying condensed carbon morphologies at different locations and times in each detonation. The detonations could be timed in such a way that the spatial and temporal dependence of the carbon morphology could be superimposed onto radiography images collected at the same point in time. The complementary approach is able to show that the carbon condensates are much larger when formed in the elevated temperature and pressure conditions near the location of colliding detonation fronts. Thermochemical modeling suggests that these larger particulates form either in the diamond phase or on the liquidus line of the carbon phase diagram. The increase in size observed by SAXS may correlate well with the increased residence time deeply in the diamond phase. Finally, these particulates can be described as nano-sized phases with some surface texture or otherwise near-surface intra-particle heterogeneity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗