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DOE OSTI2024

Data-scarce surrogate modeling of shock-induced pore collapse process

Understanding the mechanisms of shock-induced pore collapse is of great interest in various disciplines in sciences and engineering, including materials science, biological sciences, and geophysics. However, numerical modeling of the complex pore collapse processes can be costly. To this end, a strong need exists to develop surrogate models for generating economic predictions of pore collapse processes. Here, in this work, we study the use of a data-driven reduced-order model, namely dynamic mode decomposition, and a deep generative model, namely conditional generative adversarial networks, to resemble the numerical simulations of the pore collapse process at representative training shock pressures. Since the simulations are expensive, the training data are scarce, which makes training an accurate surrogate model challenging. To overcome the difficulties posed by the complex physics phenomena, we make several crucial treatments to the plain original form of the methods to increase the capability of approximating and predicting the dynamics. In particular, physics information is used as indicators or conditional inputs to guide the prediction. In realizing these methods, the training of each dynamic mode composition model takes only around 30 s on CPU. In contrast, training a generative adversarial network model takes 8 h on GPU. Moreover, using dynamic mode decomposition, the final-time relative error is around 0.3% in the reproductive cases. We also demonstrate the predictive power of the methods at unseen testing shock pressures, where the error ranges from 1.3 to 5% in the interpolatory cases and 8 to 9% in extrapolatory cases.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Machine learning for fundamental spectroscopic and thermodynamic data of actinides and lanthanides

Accurately modeling optical spectra with absolute radiometric intensities is vital for nuclear forensics applications that depend on characterizing optical emissions from energetic nuclear phenomena. This requires precise knowledge of the individual atomic transition probabilities, known as Einstein A-coefficients, for each emission line. Obtaining these values theoretically or experimentally is often impractical due to the complex electronic structures and the number of transitions involved in atoms relevant to nuclear applications. In this study, we explore the use of machine learning to predict the Einstein A coefficients for atomic transitions. Seven models were evaluated that ranged from deep learning to decision tree algorithms, and found that gradient boosting performed best, specifically the Extreme Gradient Boosting (XGB) architecture, achieving a precision of 86% across transitions of 36 elements. Furthermore, the model was cross-validated using published transition probabilities reported in the literature and applied to estimate Pu plasma temperatures from a previous experiment conducted at Savannah River National Laboratory.

Atomic spectroscopy↗

DOE OSTI2022

Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. How to screen promising compositions from such an ample design space for targeted properties is a grand challenge. In this report, we demonstrate the state-of-the-art deep learning technology—convolutional neural network (CNN)—in predicting path-dependent vacancy migration energy barrier spectra, which are critical to diffusion behavior and many high-temperature properties, in the hyperdimensional composition space of MPEAs. The developed CNN model, fully capturing local chemical features surrounding each vacancy, accurately and efficiently predicts migration energy barrier of MPEAs with different degrees of chemical short-range order and at any unseen compositions. By varying the size of the local region encapsulating vacancy in the CNN model, we reveal that the length scale influencing vacancy migration is surprisingly extensive, up to its six nearest neighboring shells. The efforts of the CNN model make it promising for developing a database of diffusion barriers for various MPEA systems, which would have profound implications for accelerating alloy screening and discovering new compositions with desirable properties.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Phase-field modeling and experiments of dynamic fracture in single crystal quartz

Predicting the onset and characteristics of brittle fracture is important for a wide range of engineering and geological material applications. In this paper, we study important aspects of brittle fracture in α-quartz by phase-field modeling and experiments using a top-down approach. In the modeling framework, the work term in the Griffith energy balance is replaced with internal energy contributions that represent surface energy, thermal energy, and elastic strain energy stored in defects. This allows parametrization of individual energy contributions in terms of internal state variables and keeps track of energy partitioning after the onset of fracture. The path and history dependence of fracture is included in evolution laws for internal state variables, e.g., entropy evolution, while the energy remains a true potential. In the experimental part, dynamic compression experiments coupled with X-ray phase contrast imaging are performed on cube-like samples with a hole. In the top-down analysis, dynamic compression and three point bending experiments from the literature are simulated with the developed phase-field damage model. In conclusion, the fitted model highlights the strain rate, size, and stress state dependence of damage nucleation and evolution in single crystal α-quartz.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A novel semi-structured kinetic model of methanotroph-photoautotroph cocultures for biogas conversion

Through metabolic coupling of methane oxidation and oxygenic photosynthesis, methanotroph-photoautotroph (M-P) cocultures offer a highly promising technology platform for biogas conversion. However, there has not been any quantitative modeling of the coculture growth kinetics. This is mainly due to the inherent difficulty associated with real time characterization of the M-P cocultures and the complex interactions such as the cross-feeding mechanism within the coculture. To address this challenge, we recently developed a novel experimental-computational (E-C) protocol to accurately characterize the M-P coculture in real-time, and validated its accuracy through cell counting. Enabled by the E-C protocol, this work presents the very first kinetic model for M-P cocultures. By explicitly modeling the exchange of in situ produced O 2 /CO 2 within the M-P coculture and coupling the individual biomass growth with mass transfer between the gas and liquid phases, the semi-structured kinetic model accurately predicts the growth dynamics of the M-P coculture under a wide range of growth conditions. The proposed model is validated by a series of wet-lab experiments using Methylomicrobium buryatense 5GB1 - Arthrospira platensis as the model coculture. Although it has been speculated that there may exist other emergent metabolic interactions within the M-P coculture, in addition to the exchange of in situ produced O 2 /CO 2 , there has not been any experimental validation prior to this study. By integrating designed experiments with the semi-structured kinetic model, this study is the first to confirm the existence of the additional emergent metabolic exchanges within the coculture. Furthermore, this study further quantifies the effect of these unknown metabolic interactions on the growth of both species in the model coculture, supporting further research to identify these exchanged metabolites for metabolic engineering.

42 ENGINEERING↗

DOE OSTI2025

Thermodynamic modeling of countercurrent chemical looping reverse water gas shift process for redox material screening

The reverse water gas shift (RWGS) reaction is a key pathway for CO 2 utilization, particularly within Power-to-X process chains aimed at sustainable fuel and chemical production. Countercurrent chemical looping (CL-RWGS) using non-stoichiometric oxides can overcome equilibrium limitations of conventional RWGS reactors, enabling significantly higher CO 2 conversions. However, modeling the limiting performance of such systems is challenging due to their multiphase nature and coupled spatial and temporal variation in chemical composition. In this work, we present a discretized batch equilibrium model that simulates CL-RWGS reactors as a series of localized equilibrium exchanges between gas and solid elements. The model is numerically stable, computationally efficient, and free of kinetic source terms, making it well-suited for parametric studies and system-level integration. It is validated against established convection–diffusion models and shown to predict reasonable upper bounds on experimental results. Application of the model to a range of oxygen carrier materials identifies cerium–zirconium solid solutions, particularly Ce 0.80 Zr 0.20 O 2 , as a promising class offering superior oxygen storage characteristics compared to state-of-the-art La 0.6 Sr 0.4 FeO 3 . This framework provides a robust platform for materials screening, reactor sizing, and performance optimization in chemical looping systems. The model implementation is available as open-source software to support further research and development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods

Here, this work uses machine learning (ML) to complement HEAT (Heat flux Engineering Analysis Toolkit) by developing 3-D footprint surrogate models for fast and accurate heat load calculations in the divertor of the SPARC tokamak. The focus is on shadowed regions, or magnetic shadows, caused by the 3-D geometry of plasma-facing components (PFCs). ML classifiers are employed to create a surrogate model for HEAT generated shadow masks, predicting these shadow masks and divertor heat flux profiles based on a diverse range of equilibria and only the plasma current, safety factor(q95) at the edge, and magnetic flux angles as input parameters. The ultimate goal is to integrate the model for real-time control and future operational decisions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Characteristic of cryogenic hydrogen flames from high-aspect ratio nozzles

Unintentional leaks at hydrogen fueling stations have the potential to form hydrogen jet flames, which pose a risk to people and infrastructure. The heat flux from these jet flames are often used to develop separation distances between hydrogen components and buildings, lot-lines, etc. The heat flux and visible flame length is well understood for releases from round nozzles, but real unintended leaks would be expected to be from higher aspect-ratio cracks. Here, we measured the visible flame length and heat-flux characteristics of cryogenic hydrogen flames from high-aspect ratio nozzles. Heat flux measurements from 5 radiometers were used to assess the single-point vs the multi-point methods for interpretation of heat flux sensor data, finding the axial distance of the sensor for a single-point heat flux measurement to be important. We compare the flame length and heat flux data to flames of both cryogenic and compressed hydrogen from round nozzles. The aspect ratio of the release does not affect the flame length or heat flux significantly, for a given mass flow under the range of conditions studied. The engineering correlations presented in this work enable the prediction of flame length and heat flux which can be used to assess risk at hydrogen fueling stations with liquid hydrogen and develop science-based separation distances for these stations.

08 HYDROGEN↗

DOE OSTI2020

Intermediate Species in the Crystallization of Sodium Aluminate Hydroxy Hydrates

In highly alkaline “water-in-salt” Na 2 O/Al 2 O 3 /H 2 O solutions where the monomeric Al(OH) 4 - anion dominates, isolation of transitional species that seed crystallization of sodium aluminate salt hydrates has been challenging. For example, discrimination of dimeric [e.g., Al 2 O(OH) 6 2- ] from species via 27 Al nuclear magnetic resonance (NMR) spectroscopy is limited via fast interconversion through hydrolysis and condensation reactions. Despite this, using magic-angle spinning NMR ( 27 Al MAS NMR) at high magnetic field strengths (14.1 and 20.0 T) on crystals of nonasodium bis(hexahydroxyaluminate) trihydroxide hexahydrate [NSA, Na 9 [Al(OH) 6 ] 2 ·3(OH)·6(H 2 O)], we observed a 27 Al resonance consistent with Na 2 Al 2 O(OH) 6 . This tetrahedral dimeric species was also identified in the mother liquor by Raman spectroscopy while remaining expectedly unresolved by high-field liquid-state 27 Al NMR. Its substantial abundance in the mother liquor and as either an interstitial or an adsorbate on precipitated solids suggest that it plays a crucial role in NSA crystallization. Here, our detailed characterization, which includes single-pulse 27 Al MAS NMR, nutation 27 Al MAS NMR, and multiple quantum 27 Al MAS NMR, provides new molecular insights into how aluminate transitions from tetrahedral in solution to octahedral in solid. This understanding could ultimately lead to better predictions of aluminum phase change behavior in caustic sodium hydroxide with wide-ranging technological and environmental implications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Advances in Clayff Molecular Simulation of Layered and Nanoporous Materials and Their Aqueous Interfaces

As a general-purpose force field for molecular simulations of layered materials and their fluid interfaces, Clayff continues to see broad usage in atomistic computational modeling for numerous geoscience and materials science applications due to its (1) success in predicting properties of bulk nanoporous materials and their interfaces, (2) transferability to a range of layered and nanoporous materials, and (3) simple functional form which facilitates incorporation into a variety of simulation codes. Here, we review applications of Clayff to model bulk phases and interfaces not included in the original parameter set and recent modifications for modeling surface terminations such as hydroxylated nanoparticle edges. Overall, we conclude with a discussion of expectations for future developments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Coverage-Dependent Adsorption of Hydrogen on Fe(100): Determining Catalytically Relevant Surface Structures via Lattice Gas Models

Hydrogen adatoms are a critical surface species for several reactions catalyzed by Fe surfaces such as Fischer–Tropsch synthesis, ammonia synthesis, and the hydrodeoxygenation of biomass-derived oxygenates. Parameterizing the energetics for H/Fe in terms of both coverage and configuration space can significantly aid in the development of multiscale models as well as provide atomic level insight into the dominant surface structures present under realistic reaction conditions. Here, we construct a lattice gas model for H/Fe(100), where the lateral interactions are determined from first-principles using density functional theory. Using 950 symmetrically unique H/Fe(100) configurations, we generate a cluster expansion with a predictive accuracy in terms of surface energy of 3.8 meV/site over a coverage range from 0 to 3 monolayers. Ten electronic ground state structures are identified from this thorough scan (including the structures at 0 and 3 monolayers), which were subsequently used to generate ab initio phase diagrams under a range of temperatures and pressures. Under reaction conditions typical of Fischer–Tropsch synthesis, ammonia synthesis, and biomass oxygenate hydrodeoxygenation, we find that the 1.0 monolayer structure is dominant. Furthermore, examination of the total H–H lateral interactions for the H/Fe(100) electronic ground state structures shows that H/Fe(100) can be accurately modeled via a mean-field ideal lattice gas model for coverages less than 1.0 monolayers. Altogether, this work enables the incorporation of H–H lateral interactions on Fe(100) into multiscale models, via either mean-field or site-dependent techniques, and provides atomic insight into the catalytically relevant H/Fe(100) structures for a range of heterogeneous reactions.

09 BIOMASS FUELS↗

DOE OSTI2026

Substrate-Directed Dimensional and Phase Control of Peptide Assemblies on Two-Dimensional van der Waals Materials

Understanding and controlling biomolecular self-assembly on van der Waals (vdW) materials has the potential to advance hybrid bioelectronic devices by enabling precise tuning of the interface and modulation of the resulting electronic properties of the biomolecule-vdW heterostructure. However, how surface properties of vdW materials direct biomolecule assembly remains poorly understood. To fill this knowledge gap, we investigated the assembly of a peptide known to assemble into two-dimensional (2D) crystalline films on MoS 2 on three representative vdW surfaces: WS 2 , MoS 2 , and highly oriented pyrolytic graphite (HOPG). Using in situ atomic force microscopy (AFM), we find that assembly is substrate-dependent, resulting in multilayers on WS 2 , monolayers on MoS 2 , and multiple coexisting phases on HOPG. WS 2 exhibits a higher negative charge, strong long-range electrostatic interactions, and extensive hydration layering that may promote multilayer stacking. In contrast, MoS 2 has stronger short-range interactions with the peptides but much weaker long-range interactions and hydration structure, which may favor monolayer formation. Molecular dynamics simulations predict a corresponding switch from monolayer to multilayer aggregates of the adsorbed monomers, reflected in their relative mobilities. On hydrophobic HOPG, the peptides bind most strongly and remain as monomers with high surface mobility. The peptide dimers comprising the basic unit of the crystals are more compact on HOPG, which has a smaller lattice constant than WS 2 or MoS 2 , suggesting strain contributes to stabilizing multiple phases. Our results provide mechanistic insights into how surface charge and hydration structure, and the lattice structure of the substrates governs peptide assembly on vdW materials, offering a framework to rationally control the 2D peptide-vdW heterostructures.

Molecular dynamics simulations↗

DOE OSTI2024

Discovery of atomic clock-like spin defects in simple oxides from first principles

Virtually noiseless due to the scarcity of spinful nuclei in the lattice, simple oxides hold promise as hosts of solid-state spin qubits. However, no suitable spin defect has yet been found in these systems. Using high-throughput first-principles calculations, we predict spin defects in calcium oxide with electronic properties remarkably similar to those of the NV center in diamond. These defects are charged complexes where a dopant atom — Sb, Bi, or I — occupies the volume vacated by adjacent cation and anion vacancies. The predicted zero phonon line shows that the Bi complex emits in the telecommunication range, and the computed many-body energy levels suggest a viable optical cycle required for qubit initialization. Notably, the high-spin nucleus of each dopant strongly couples to the electron spin, leading to many controllable quantum levels and the emergence of atomic clock-like transitions that are well protected from environmental noise. Specifically, the Hanh-echo coherence time increases beyond seconds at the clock-like transition in the defect with 209 Bi. Our results pave the way to designing quantum states with long coherence times in simple oxides, making them attractive platforms for quantum technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor

Abstract 2D semiconductors offer a promising pathway to replace silicon in next-generation electronics. Among their many advantages, 2D materials possess atomically-sharp surfaces and enable scaling the channel thickness down to the monolayer limit. However, these materials exhibit comparatively lower charge carrier mobility and higher contact resistance than 3D semiconductors, making it challenging to realize high-performance devices at scale. In this work, we search for high-mobility 2D materials by combining a high-throughput screening strategy with state-of-the-art calculations based on the ab initio Boltzmann transport equation. Our analysis singles out a known transition metal dichalcogenide, monolayer WS 2 , as the most promising 2D semiconductor, with the potential to reach ultra-high room-temperature hole mobilities in excess of 1300 cm 2 /Vs should Ohmic contacts and low defect densities be achieved. Our work also highlights the importance of performing full-blown ab initio transport calculations to achieve predictive accuracy, including spin–orbital couplings, quasiparticle corrections, dipole and quadrupole long-range electron–phonon interactions, as well as scattering by point defects and extended defects.

Chemistry↗

DOE OSTI2020

Unveiling the strong interaction among hadrons at the LHC

One of the key challenges for nuclear physics today is to understand from first principles the effective interaction between hadrons with different quark content. First successes have been achieved using techniques that solve the dynamics of quarks and gluons on discrete space-time lattices. Experimentally, the dynamics of the strong interaction have been studied by scattering hadrons off each other. Such scattering experiments are difficult or impossible for unstable hadrons and so high-quality measurements exist only for hadrons containing up and down quarks. Here we demonstrate that measuring correlations in the momentum space between hadron pairs produced in ultrarelativistic proton–proton collisions at the CERN Large Hadron Collider (LHC) provides a precise method with which to obtain the missing information on the interaction dynamics between any pair of unstable hadrons. Specifically, we discuss the case of the interaction of baryons containing strange quarks (hyperons). We demonstrate how, using precision measurements of proton–omega baryon correlations, the effect of the strong interaction for this hadron–hadron pair can be studied with precision similar to, and compared with, predictions from lattice calculations. The large number of hyperons identified in proton–proton collisions at the LHC, together with accurate modelling of the small (approximately one femtometre) inter-particle distance and exact predictions for the correlation functions, enables a detailed determination of the short-range part of the nucleon-hyperon interaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Search for charged lepton flavor violating 𝑍 and 𝑍′ boson decays in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search for flavor violating decays of the 𝑍 boson to charged leptons is performed using data from proton-proton collisions at $\sqrt{s}$ =13 TeV collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 138 fb −1 . Each of the decays 𝑍 → 𝑒⁢𝜇, 𝑍 → 𝑒⁢𝜏, and 𝑍 → 𝜇⁢𝜏 is considered. The data are consistent with the backgrounds expected from standard model processes. For the 𝑍 → 𝑒⁢𝜇 channel the observed (expected) 95% confidence level upper limit on the branching fraction is 1.9⁢(2.0) × 10 −7 , which is the most stringent direct limit to date on this process; the corresponding limits for the 𝑍 → 𝑒⁢𝜏 and 𝑍 → 𝜇⁢𝜏 channels are 13.8⁢(11.4) × 10 −6 and 12.0⁢(5.3) × 10 −6 , respectively. Additionally, the 𝑒⁢𝜇 final state is used to search for lepton flavor violating decays of 𝑍′ resonances in the mass range from 110 to 500 GeV. No significant excess is observed above the predicted background levels.

Hayrapetyan, A. [Yerevan Physics Institute]↗

DOE OSTI2021

Implications of PREX-2 on the electric dipole polarizability of neutron-rich nuclei

Background: The recent announcement by the PREX collaboration of an unanticipated thick neutron skin in 208 Pb ($R^{208}_{skin}$) has challenged the understanding of neutron-rich matter in the vicinity of nuclear saturation density. Whereas earlier constraints indicate that the symmetry energy is relatively soft, the PREX-2 result seems to suggest the opposite. Purpose: To confront constraints on the symmetry energy obtained from measurements of the electric dipole polarizability against those informed by the PREX-2 measurement of $R^{208}_{skin}$ and by the correlations that it entails. Methods: Here, covariant energy density functionals informed by the properties of finite nuclei are used to compute the electric dipole response of 48 Ca, 68 Ni, 132 Sn, and 208 Pb. The set of functionals used in this work are consistent with experimental data, yet are flexible enough in that they span a wide range of values of $R^{208}_{skin}$ Results: It is found that theoretical predictions of the electric dipole polarizability that are consistent with the PREX-2 measurement systematically overestimate the corresponding values extracted from the direct measurements of the distribution of electric dipole strength. Conclusions: The neutron skin thickness of 208 Pb extracted from parity violating electron scattering and the electric dipole polarizability measured in photoabsorption experiments are two of the cleanest experimental tools used to constrain the symmetry energy around nuclear saturation density. However, the recent value of $R^{208}_{skin}$ that suggests a fairly stiff symmetry energy stands in stark contrast to the conclusions derived from the electric dipole polarizability. At present, I offer no solution to this dilemma.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

How to falsify string theory at a collider

The string landscape accommodates a broad range of possible effective field theories. This poses a challenge for extracting verifiable predictions as well as falsifiable signatures of string theory. Motivated by these considerations, in this work we observe that all known stringy standard models support only low-dimensional representations of the gauge group. While it is in principle possible to produce contrived models with higher-dimensional representations, these generically appear in a tower of states with lighter ones in lower-dimensional representations, i.e., not in isolation. With this in mind, we consider the phenomenologically well-motivated scenario given by adding a single Majorana field in a real, 𝑛-dimensional representation of 𝑆⁢𝑈⁢(2) 𝐿 with 𝑛≥5 and nothing else . This scenario is not realized in any known string construction, and we conjecture that this is true of string theory in general. Detection of this scenario would thus amount to falsifying the (known) string landscape. We recast existing LHC searches for new electroweak states to extract updated bounds on this class of scenarios. Improved limits from future colliders and dark matter detection experiments provide additional routes to potentially falsifying string theory.

extensions of fermion sector↗