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DOE OSTI2025

A high-order computational framework for particle-resolved simulations of disperse multiphase flows

This work presents a high-order numerical approach for particle-resolved simulations of disperse multiphase flows, where the Navier-Stokes equations for fluid flow are solved using a high-order spectral element method in the Eulerian framework, and the particle phase is directly simulated with a discrete element method. The coupling between particles and fluids is explicitly handled using an adapted direct-forcing immersed boundary method. Unlike the conventional schemes, a high-order barycentric Lagrange interpolation method and a Gaussian projection kernel are used to ensure accurate momentum exchange between local boundary points and surrounding fluid nodes in the framework of high-order fluid solver. Benchmark tests of increasing complexity are conducted to demonstrate the accuracy and efficiency of our method. Here, it is found that our approach exhibits an excellent convergence performance, as the fluid element/grid is refined and the number of boundary points increases. Compared to conventional low-order methods, the proposed high-order framework enables the use of substantially larger fluid elements while maintaining high accuracy in modeling fluid-particle interactions, owing to the enhanced resolution of high-order basis functions. Moreover, since the primary unknowns are stored at element or grid nodes, the high-order approach offers improved efficiency in both CPU memory usage and total computational cost.

42 ENGINEERING↗

DOE OSTI2025

Numerical schemes for 3-wave kinetic equations: A complete treatment of the collision operator

In our previous work Walton and Tran (2023), numerical schemes for a simplified version of 3-wave kinetic equations, in which only the simple forward-cascade terms of the collision operators are kept, have been successfully designed, especially to capture the long time dynamics of the equation given the multiple blow-up time phenomenon. In this second work in the series, we propose numerical treatments for the complete 3-wave kinetic equations, in which the complete, much more complicated collision operators are fully considered based on a novel conservative form of the equation. Here we then derive an implicit finite volume scheme to solve the equation. The new discretization uses an adaptive time-stepping method which allows for the simulations to be carried to very long times. Our computed solutions are compared with previously derived long-time asymptotic estimates for the decay rate of total energy of time-dependent solutions of 3-wave kinetic equations and found to be in excellent agreement.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Three-Dimensional Mass Spectrometric Imaging of Biological Structures Using a Vacuum-Compatible Microfluidic Device

Three-dimensional (3D) molecular imaging of biological structures is important for a wide range of research. In recent decades, secondary ion mass spectrometry (SIMS) has been recognized as a powerful technique for both two-dimensional (2D) and 3D molecular imaging. Sample fixations (e. g., chemical fixation and cryogenic fixation methods) are necessary to adapt biological samples to the vacuum condition in the SIMS chamber, which has been demonstrated to be non-trivial and less controllable, thus limiting the wider application of SIMS on 3D molecular analysis of biological samples. Our group recently developed in situ liquid SIMS that offers great opportunities for the molecular study of various liquids and liquid interfaces. In this work, we demonstrate that a further development of the vacuum-compatible microfluidic device used in in situ liquid SIMS provides a convenient freeze-fixation of biological samples and leads to more controllable and convenient 3D molecular imaging. The special design of this new vacuum-compatible liquid chamber allows an easy determination of sputter rates of ice, which is critical for calibrating the depth scale of frozen biological samples. Sputter yield of a 20 keV Ar 1800 + ion on ice has been determined as 1500 (± 8%) water molecules per Ar 1800 + ion, consistent with our results from molecular dynamics simulations. Moreover, using the information of ice sputter yield, we successfully conduct 3D molecular imaging of frozen homogenized milk and observe network structures of interesting organic and inorganic species. Finally, taken together, our results will significantly benefit various research fields relying on 3D molecular imaging of biological structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Interfacial and Activation Energies of Environmentally Abundant Heterogeneously Nucleated Iron(III) (Hydr)oxide on Quartz

Poorly crystalline iron(III) (hydr)oxide nanoparticles are ubiquitous in environmental systems and play a crucial role in controlling the fate and transport of contaminants. Yet, the thermodynamic and kinetic parameters, e.g., the effective interfacial (α^') and apparent activation (E a ) energies, of iron(III) (hydr)oxide nucleation on earth-abundant mineral surfaces have not been determined, which hinders an accurate prediction of iron(III) (hydr)oxide formation and its interactions with other toxic or reactive ions. Here, for the first time, we report experimentally obtained α^' and E a for iron(III) (hydr)oxide nucleation on quartz mineral surfaces, by employing a flow-through, time-resolved grazing incidence small angle X-ray scattering (GISAXS). GISAXS enabled the in situ detection of iron(III) (hydr)oxide nucleation rates under different supersaturations (σ, achieved by varying pH 3.3-3.6) and temperatures (12-35ºC). By quantitative analyses based on classical nucleation theory, α^' was obtained to be 34.6 mJ/m 2 and E a was quantified as 32.8 kJ/mol. The fundamental thermodynamic and kinetic parameters obtained here will advance our fundamental understanding of the surface chemistry and nucleation behavior of iron(III) (hydr)oxides in subsurface and water treatment systems, as well as their effects on the fate and transport of pollutants in natural and engineered water systems. The in situ flow-through GISAXS method can also be adapted to quantify thermodynamic and kinetic parameters at interfaces for many important solid–liquid environmental systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Combined Force-Frequency Sampling for Simulation of Systems Having Rugged Free Energy Landscapes.

An adaptive, machine learning-based sampling method is presented for simulation of systems having rugged, multidimensional free energy landscapes. The method's main strength resides in its ability to learn both from the frequency of visits to distinct states and the generalized force estimates that arise in a system as it evolves in phase space. This is accomplished by introducing a self-integrating artificial neural network, which generates an estimate of the free energy directly from its derivatives. The usefulness of the proposed combined approach is examined in the context of two concrete examples, namely, an alanine dipeptide molecule in water and a polymer diffusing through a narrow pore. This new method is found to be robust, faster, and more accurate than approaches that rely only on frequency-based or generalized force-based estimations. After combining the proposed approach with overfill protection and support for sparse data storage and training, the method is shown to be more effective than comparable, previously available techniques and capable of scaling efficiently to larger numbers of collective variables.

Sevgen, Emre↗

DOE OSTI2021

Correlated Response of Protein Side-Chain Fluctuations and Conformational Entropy to Ligand Binding

The heterogeneous fast side-chain dynamics of proteins plays crucial roles in molecular recognition and binding. Site-specific NMR experiments quantify these motions by measuring the model-free order parameter (O axis 2 ) on a scale of 0 (most flexible) to 1 (least flexible) for each methyl-containing residue of proteins. In this work, we have examined ligand-induced variations in the fast side-chain dynamics and conformational entropy of calmodulin (CaM) using five different CaM–peptide complexes. O axis 2 of CaM in the ligand-free (O axis,U 2 ) and ligand-bound (O axis,B 2 ) states are calculated from molecular dynamics trajectories and conformational energy surfaces obtained using the adaptive biasing force (ABF) method. ΔO axis 2 = O axis,B 2 – O axis,U 2 follows a Gaussian-like unimodal distribution whose second moment is a potential indicator of the binding affinity of these complexes. The probability for the binding-induced O axis,U 2 → O axis,B 2 transition decreases with increasing magnitude of ΔO axis 2 , indicating that large flexibility changes are improbable for side chains of CaM after ligand binding. A linear correlation established between ΔOaxis2 and the conformational entropy change of the protein makes possible the determination of the conformational entropy of binding of protein–ligand complexes. The results not only underscore the functional importance of fast side-chain fluctuations but also highlight key motional and thermodynamic correlates of protein–ligand binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Competition between Hydrogen and Chalcogen Bonding in Homodimers of Chalcogen Hydrides (H 2 X) 2 , X = O, S, Se, Te

The structural and chemical bonding motifs manifested by the competition between hydrogen and chalcogen bonding in the homodimers of chalcogen hydrides (H 2 X) 2 , where X = O, S, Se, Te have been characterized using high-level electronic structure calculations and analysis of the electron density based on Quasi-atomic orbital (QUAO) and the Symmetry-adapted perturbation theory (SAPT) methods. The QUAO analysis clearly identifies a three-center interaction responsible for either hydrogen or chalcogen bonds: in the former, the σ-bond connecting the donor and hydrogen atom participating in the hydrogen bond interacts with the lone pair on the nucleophile acceptor via the hydrogen atom, while in the latter this same σ-bond interacts with the nucleophile lone pair via the donor chalcogen. The number of minimum energy structures increase dramatically from one for (H 2 O) 2 , three for (H 2 S) 2 , four for (H 2 Se) 2 , and finally six for (H 2 Te) 2 . Furthermore, the emergence of the chalcogen-bonded arrangements appears for (H 2 S) 2 with their subsequent energetic stabilization over the hydrogen-bonded minima manifesting in (H 2 Se) 2 and (H 2 Te) 2 . In particular, one of the (H 2 S) 2 , two of the (H 2 Se) 2 , and three of the (H 2 Te) 2 dimers are chalcogen bonded. Induction plays a small but important role in stabilizing hydrogen over chalcogen-bonded structures, while dispersion is more important for chalcogen bonds.

Chemical structure↗

DOE OSTI2025

Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Abstract Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model‐data integration workflow to address uncertainty in groundwater response to beaver‐induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning‐based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down‐valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down‐valley underflow, from 2 during dry pond periods to 20 during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down‐valley underflow, minimizes the effect of beaver‐induced inundation on water quality. We emphasize the need for field‐scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response. Plain Language Summary Beavers change the flow of water in river corridors by creating ponds, expanding wetlands, and flooding floodplains. This increases surface water area, promotes plant growth, and enhances biodiversity. However, the impact of this flooding on groundwater flow is not well understood, especially in mountainous areas with gravel layers where water moves easily beneath soil. In this study, we used numerical modeling to investigate how beaver ponds influence groundwater in a mountainous floodplain of the Upper Colorado River Basin. We adapted a machine learning method to validate our numerical models using multiple field data sets. Our findings show that beaver ponds significantly increase vertical water flow from the soil to the gravel during wet periods, compared to when the ponds are fully drained. The study also highlights the importance of floodplain structure in controlling both water flow in gravel layers along the river direction and vertical flow from the soil to the gravel with the presence of beavers. To reduce uncertainty in groundwater response, we emphasize the need for more field‐scale measurements of floodplain structure, hydraulic properties, and evapotranspiration changes. Key Points Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters

Wang, Lijing↗

DOE OSTI2021

Probing subwavelength in-plane anisotropy with antenna-assisted infrared nano-spectroscopy

Infrared nano-spectroscopy based on scattering-type scanning near-field optical microscopy (s-SNOM) is commonly employed to probe the vibrational fingerprints of materials at the nanometer length scale. However, due to the elongated and axisymmetric tip shank, s-SNOM is less sensitive to the in-plane sample anisotropy in general. In this article, we report an easy-to-implement method to probe the in-plane dielectric responses of materials with the assistance of a metallic disk micro-antenna. As a proof-of-concept demonstration, we investigate here the in-plane phonon responses of two prototypical samples, i.e. in (100) sapphire and x-cut lithium niobate (LiNbO 3 ). In particular, the sapphire in-plane vibrations between 350 cm–1 to 800 cm–1 that correspond to LO phonon modes along the crystal b- and c-axis are determined with a spatial resolution of < λ/10, without needing any fitting parameters. In LiNbO 3 , we identify the in-plane orientation of its optical axis via the phonon modes, demonstrating that our method can be applied without prior knowledge of the crystal orientation. Furthermore, our method can be elegantly adapted to retrieve the in-plane anisotropic response of a broad range of materials, i.e. subwavelength microcrystals, van-der-Waals materials, or topological insulators.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Adaptive finite differencing in high accuracy electronic structure calculations

Abstract A multi-order Adaptive Finite Differencing (AFD) method is developed for the kinetic energy operator in real-space, grid-based electronic structure codes. It uses atomic pseudo orbitals produced by the corresponding pseudopotential codes to optimize the standard finite difference (SFD) operators for improved precision. Results are presented for a variety of test systems and Bravais lattice types, including the well-known Δ test for 71 elements in the periodic table, the Mott insulator NiO, and borax decahydrate, which contains covalent, ionic, and hydrogen bonds. The tests show that an 8th-order AFD operator leads to the same average Δ value as that achieved by plane-wave codes and is typically far more accurate and has a much lower computational cost than a 12th-order SFD operator. The scalability of real-space electronic calculations is demonstrated for a 2016-atom NiO cell, for which the computational time decreases nearly linearly when scaled from 18 to 144 CPU-GPU nodes.

Briggs, E. L. (ORCID:0000000343983492)↗

DOE OSTI2024

Unleashed from constrained optimization: quantum computing for quantum chemistry employing generator coordinate inspired method

Hybrid quantum-classical approaches offer potential solutions to quantum chemistry problems, yet they often manifest as constrained optimization problems. Here, we explore the interconnection between constrained optimization and generalized eigenvalue problems through the Unitary Coupled Cluster (UCC) excitation generators. Inspired by the generator coordinate method, we employ these UCC excitation generators to construct non-orthogonal, overcomplete many-body bases, projecting the system Hamiltonian into an effective Hamiltonian, which bypasses issues such as barren plateaus that heuristic numerical minimizers often encountered in standard variational quantum eigensolver (VQE). Diverging from conventional quantum subspace expansion methods, we introduce an adaptive scheme that robustly constructs the many-body basis sets from a pool of the UCC excitation generators. This scheme supports the development of a hierarchical ADAPT quantum-classical strategy, enabling a balanced interplay between subspace expansion and ansatz optimization to address complex, strongly correlated quantum chemical systems cost-effectively, setting the stage for more advanced quantum simulations in chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Producing two-dimensional dust clouds and clusters using a movable electrode for complex plasma and fundamental physics experiments

We report a Bidirectional Electrode Control Arm Assembly (BECAA) for precisely manipulating dust clouds levitated above the powered electrode in RF plasmas. The reported techniques allow the creation of perfectly 2D dust layers by eliminating off-plane particles by moving the electrode from outside the plasma chamber without altering the plasma conditions. Here, the tilting and moving of electrodes using BECAA also allows the precise and repeatable elimination of dust particles one by one to achieve any desired number of grains N without trial and error. Simultaneously acquired top and side view images of dust clusters show that they are perfectly planar or 2D. A demonstration of clusters with N = 1–28 without changing the plasma conditions is presented to show the utility of BECAA for complex plasma and statistical physics experimental design. Demonstration videos and 3D printable part files are available for easy reproduction and adaptation of this new method to repeatably produce 2D clusters in existing RF plasma chambers.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Digitally manufactured air plasma-on-water reactor for nitrate production

The sustainable production of food to support the increasing world population is one of humanity’s most pressing challenges. Plasma activated water, produced using renewable energy, can help fulfill plants’ needs in sustainable agriculture approaches. The design, implementation, and characterization of a digitally manufactured air plasma-on-water reactor (POWR) for the synthesis of nitrate as green nitrogen fertilizer is presented. The interaction of air plasma-generated reactive oxygen and nitrogen species with water produces nitrate (NO 3 - ) and related species, which are the main nitrogen-containing nutrients for plants. The mild conditions of the operation of the POWR opens the possibility to use plastics, particularly through digital manufacturing strategies such as 3D-printing, for its fabrication. A pin-to-plate reactor configuration powered by high-voltage alternating power is chosen due to its simplicity and efficacy. A computational thermal-fluid model is used to evaluate the design and attain expected operational characteristics. The experimental characterization of the POWR encompassed design and operation parameters, namely electrode-water spacing, air flow rate, and voltage level. A machine learning approach is implemented to extract and quantify characteristic features of the plasma–water interaction, such plasma volume and plasma–water interface area. Experimental results revealed that the nitrate production rate varies linearly with dimensionless plasma volume. The design, fabrication, and characterization methods presented can be adapted to other POWRs and help enable on-demand nitrogen fertilizer production at low environmental and economic cost.

Physics↗

DOE OSTI2024

Molecular NMR shieldings, J -couplings, and magnetizabilities from numeric atom-centered orbital based density-functional calculations

This paper reports and benchmarks a new implementation of nuclear magnetic resonance shieldings, magnetizabilities, and J-couplings for molecules within semilocal density functional theory, based on numeric atom-centered orbital (NAO) basis sets. NAO basis sets are attractive for the calculation of these nuclear magnetic resonance (NMR) parameters because NAOs provide accurate atomic orbital representations especially near the nucleus, enabling high-quality results at modest computational cost. Moreover, NAOs are readily adaptable for linear scaling methods, enabling efficient calculations of large systems. Here, the paper has five main parts: (1) It reviews the formalism of density functional calculations of NMR parameters in one comprehensive text to make the mathematical background available in a self-contained way. (2) The paper quantifies the attainable precision of NAO basis sets for shieldings in comparison to specialized Gaussian basis sets, showing similar performance for similar basis set size. (3) The paper quantifies the precision of calculated magnetizabilities, where the NAO basis sets appear to outperform several established Gaussian basis sets of similar size. (4) The paper quantifies the precision of computed J-couplings, for which a group of customized NAO basis sets achieves precision of ~Hz for smaller basis set sizes than some established Gaussian basis sets. (5) The paper demonstrates that the implementation is applicable to systems beyond 1000 atoms in size.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Bioinspired stability enhancement in deuterium-substituted organic–inorganic hybrid perovskite solar cells

Abstract In hybrid perovskite solar cells (PSCs), the reaction of hydrogens (H) located in the amino group of the organic A-site cations with their neighboring halides plays a central role in degradation. Inspired by the retarded biological activities of cells in heavy water, we replaced the light H atom with its abundant, twice-as-heavy, nonradioactive isotope, deuterium (D) to hamper the motion of H. This D substitution retarded the formation kinetics of the detrimental H halides in Pb-based PSCs, as well as the H bond-mediated oxidation of Sn2+ in Sn–Pb-based narrow-bandgap PSCs, evidenced by accelerated stability studies. A computational study indicated that the zero point energy of D-based formamidinium (FA) is lower than that of pristine FA. In addition, the smaller increase in entropy in D-based FA than in pristine FA accounts for the increased formation free energy of the Sn2+ vacancies, which leads to the retarded oxidation kinetics of Sn2+. In this study, we show that substituting active H with D in organic cations is an effective way to enhance the stability of PSCs without sacrificing photovoltaic (PV) performance. This approach is also adaptable to other stabilizing methods.

14 SOLAR ENERGY↗

DOE OSTI2026

Langevin and Fokker-Planck analyses for diffusion-mediated passing of circular and discorectangular species in two-dimensional channels

The propensity for pairs of diffusing species to pass each other within narrow channels or pores is of basic interest as a first-passage-type problem. It is also of relevance for solution-phase transport in nanoporous materials, and in particular for catalytic conversion reactions where high yield requires that product species can efficiently pass reactant species to exit the pores. Here, we analyze a two-dimensional model with nonoverlapping circular and discorectangular species confined to a rectangular channel, and where passing is mediated by Brownian dynamics in an implicit solvent. For narrower channels where passing is still possible, the discorectangle must align with the channel to pass the circular species. Behavior of the passing propensity, 𝑃, can be assessed by strongly damped Langevin simulations, or within an equivalent Fokker-Planck equation (FPE) formalism. The latter corresponds to a diffusion problem in a “higher-dimensional channel” with a constriction. We assess the variation of the passing propensity, 𝑃, for a broad range of channel width including its scaling just above the threshold where passing is sterically blocked. Analysis of 𝑃 versus the rotational diffusion coefficient 𝐷 𝑟 of the discorectangle reveals a significant decrease in 𝑃 for lower 𝐷 𝑟 for moderate channel width. This prompts a direct analysis of the regime where 𝐷 𝑟 → 0 , for which the FPE can be reduced to a three-dimensional diffusion problem, precise analysis of which is facilitated by adaptive-mesh finite element methods. The dependence of 𝑃 on the aspect ratio of the discorectangle is also assessed.

Rahman, Md Khaledur [Ames Laboratory (AMES), Ames,↗

DOE OSTI2022

Data-Driven Discovery of Active Nematic Hydrodynamics

Active nematics can be modeled using phenomenological continuum theories that account for the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistical description of the experiments, the relevant terms in the PDEs and their parameters are usually identified indirectly. We adapt a recently developed method to automatically identify optimal continuum models for active nematics directly from spatio-temporal data, via sparse regression of the coarse-grained fields onto generic low order PDEs. After extensive benchmarking, we apply the method to experiments with microtubule-based active nematics, finding a surprisingly minimal description of the system. Furthermore, our approach can be generalized to gain insights into active gels, microswimmers, and diverse other experimental active matter systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗