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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Møller–Plesset and Density-Fixed Adiabatic Connections for a Model Diatomic System at Different Correlation Regimes

In recent years, adiabatic connection (AC) interpolations developed within density functional theory (DFT) have been found to provide good performances in the calculation of interaction energies when used with Hartree–Fock (HF) ingredients. The physical and mathematical reasons for such unanticipated performance have been clarified, to some extent, by studying the strong-interaction limit of the Møller–Plesset (MP) AC. In this work, we calculate both the MP and the DFT AC integrand for the asymmetric Hubbard dimer, which allows for a systematic investigation of different correlation regimes by varying two simple parameters in the Hamiltonian: the external potential, Δv, and the interaction strength, U. Notably, we find that, while the DFT AC integrand appears to be convex in the full parameter space, the MP integrand may change curvature twice. Furthermore, we discuss different aspects of the second-order expansion of the correlation energy in each AC, and we demonstrate why the derivative of the λ-dependent density in the MP AC at λ = 0 (i.e., at the HF density) is zero in the model. Concerning the strong-interaction limit of both ACs in the Hubbard dimer setting, we show that the asymptotic value of the MP AC, W ∞ HF , is lower than (or equal to) its DFT analogue, W ∞ KS , if the two are compared at a given density, just like in real space. Furthermore, we also show that this is not always the case if the two quantities are compared at a given external potential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Generating Protein Structures for Pathway Discovery Using Deep Learning

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Efficient Use of an Adapting Database of Ab Initio Calculations To Generate Accurate Newtonian Dynamics

We develop and demonstrate a method to efficiently use density functional calculations to drive classical dynamics of complex atomic and molecular systems. The method has the potential to scale to systems and time scales unreachable with current ab initio molecular dynamics schemes. It relies on an adapting dataset of independently computed Hellmann–Feynman forces for atomic configurations endowed with a distance metric. The metric on configurations enables fast database lookup and robust interpolation of the stored forces. Here, we discuss mechanisms for the database to adapt to the needs of the evolving dynamics, while maintaining accuracy, and other extensions of the basic algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Real-Space Quantification of Exciton Localization in Acene Crystals Using Wannier Function Decomposition

Here, we introduce the Wannier function decomposition of excitons (WFDX) method to quantify exciton localization in solids within the ab initio Bethe-Salpeter equation framework. By decomposing each Bloch exciton wave function into products of single-particle electron and hole maximally localized Wannier functions, this real-space approach provides well-defined orbital- and spatial-resolved measures of both Frenkel and charge-transfer excitons at low computational cost. We apply WFDX to excitons in acene crystals, quantifying how the number of rings, the exciton spin state, and the center-of-mass momentum affect spatial localization. Additionally, we show how this real-space representation reflects structural nonsymmorphic symmetries that are hidden in standard reciprocal-space descriptions. We demonstrate how the WFDX framework can be used to efficiently interpolate exciton expansion coefficients in reciprocal-space and outline how it may facilitate evaluation of observables involving position operators, highlighting its potential as a general tool for both analyzing and computing excitonic properties in solids.

Tao, Zui [University of California, Berkeley, CA (↗
DOE OSTI2024

Simple and General Unitarity Conserving Numerical Real-Time Propagators of the Time-Dependent Schrödinger Equation Based on Magnus Expansion

Magnus expansion (ME) provides a general way to expand the real-time propagator of a time-dependent Hamiltonian within the exponential such that the unitarity is satisfied at any order. Here, we use this property and explicit integration of Lagrange interpolation formulas for the time-dependent Hamiltonian within each time interval and derive approximations that preserve unitarity for the differential time evolution operators of general time-dependent Hamiltonians. The resulting second-order approximation is the same as using the average of Hamiltonians for two end points of time. We identify three fourth-order approximations involving commutators of Hamiltonians at different times and also derive a sixth-order expression. A test of these approximations along with other available expressions for a two-state time-dependent Hamiltonian with sinusoidal time dependences provides information on the relative performance of these approximations and suggests that the derived expressions can serve as useful numerical tools for time evolution in time-resolved spectroscopy, quantum control, quantum sensing, real-time ab initio quantum dynamics, and open system quantum dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Canonical Approach To Generate Multidimensional Potential Energy Surfaces

A new force-based canonical approach for the accurate generation of multidimensional potential energy surfaces is demonstrated. Canonical transformations previously developed for diatomic molecules are used to construct accurate approximations to the 3-dimensional potential energy surface of the water molecule from judiciously chosen (adopting the right coordinate system) 1-dimensional planar slices that are shown to have the same canonical shape as the classical Lennard-Jones potential curve. Spline interpolation is then used to piece together the 1-dimensional canonical potential curves, to obtain the full 3-dimensional potential energy surface of a water molecule with a relative error less than 0.01. This work provides an approach to greatly reduce the computational cost of constructing potential energy surfaces in molecules from ab initio calculations. The canonical transformation techniques developed in this work illuminate a pathway to deepening our understanding of chemical bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Extent of Surface Force Additivity on Chemically Heterogeneous Substrates at Varied Orientations

Surface interactions between chemically mixed surfaces, as well as those among dissolved biomolecules, comprise distinct contributions from polar and hydrophobic moieties. These contributions are often context dependent. Approximate compliance to the Cassie additivity equation for the wetting free energies on mixed surfaces in water is, however, indicative of similarly additive forces between individual surface elements, suggesting a quadratic interpolation model for total force from the forces between pure surfaces. We use molecular dynamics/umbrella sampling simulations of parallel and nonparallel mixed surfaces with demonstrable Cassie-like behavior to verify how well the total surface force between the heterogeneous, molecularly rough surfaces can be approximated as a combination of forces among the homogeneous ones. When accounting for dissimilar distances of approach between functional groups of different types, our results for graphene surfaces with mixed methyl and nitrile coating show such a superposition to provide a reasonable first order approximation of interactions between the platelets. Deviations from additivity are more prominent in parallel-plate configurations, at high content of hydrophobic groups, and small separations. The inclusion of water polarizability does not visibly alter the observed behavior regardless of platelet orientations. The outcome of this study determines the necessary molecular conditions for observing force additivity that emphasize the context dependence of hydrophobic interaction in the presence of polar groups. This notion provides guidelines for the syntheses of new, chemically heterogeneous materials with tailored function-oriented properties in aqueous media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Computational Screening of Cationic Zeolites for n -Butane/Methane Separations Using Quantitatively Accurate First-Principles-Derived Force Fields

We developed force fields for linear alkanes in Na and Ca-exchanged zeolites based on periodic first-principles calculations with coupled cluster corrections. These force fields were validated by comparing the simulated adsorption properties with extensive experimental data. As an example of using these force fields, we screened silica and aluminosilicate zeolites for separation of n-butane/methane mixtures in several different separation processes. A key step in this screening was the use of an interpolation method to create accurate zeolite structures that take account of the lattice constant changes for different Si/Al ratios for a given zeolite topology. We focused on zeolite structures that have known synthesis routes, so the materials selected from our calculations can be tested experimentally. Several promising materials that show good separation performance were selected for more detailed simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Smooth Dispersion Is Physically Appropriate: Assessing and Amending the D4 Dispersion Model

The addition of dispersion corrections to density functionals is essential for accurate energy and geometry predictions. Among them, the D4 scheme is popular due to its low computational cost and high accuracy. However, due to its design, the D4 correction can occasionally lead to anomalies, such as unphysical curvature and bumps in the potential energy surface. We find these anomalies are common in the D4 model, although observable consequences are rarer than in the D3 model for reasons we explain. Nevertheless, we uncover instances of unphysical local minima and stationary points with the D4 scheme and propose two solutions that yield smoother dispersion energy as a function of nuclear position. One is trivial to implement, based on a smoother reparametrization of Gaussian weighting (D4S) to find the effective coordination number. The other replaces Gaussian weighting with soft linear interpolation (D4SL). These new approaches usually remove artificial extremum points, while maintaining accuracy.

Tkachenko, Nikolay V↗
DOE OSTI2026

Minimization of Disorder as a Key Design Principle for Natural Sizes of Light Harvesting 2 Complexes

The light harvesting 2 (LH2) complex of purple bacteria has excellent energy conversion efficiency. Clarifying the design principle behind such efficiency at the atomistic level is crucial for understanding its structure–function relationship and can be utilized for the design of artificial light harvesting systems. To this end, we conducted comprehensive computational investigation of the dynamical and statistical nature of electronic excited states of pigment molecules in a natural LH2 complex with 9-fold symmetry and its two non-natural in silico analogues with 6- and 12-fold symmetries. To ensure reliable and efficient all-atomistic molecular dynamics simulations, we combined a well established interpolation approach for the construction of the potential energy surface with a neural network machine learning approach. Outcomes of these calculations clarify that non-natural forms of LH2-type complexes have significantly larger quasistatic disorder than those for the natural one. In addition, non-natural systems have more disruptions of the hydrogen bonding, underscoring its crucial role for reducing the disorder. On the other hand, local environmental dynamics are relatively insensitive to the structural changes although there is moderate enhancement in the anharmonic or interatomic components for the synthetic ones. These findings based on all-atomistic simulations provide direct computational evidence that the structure and sizes of natural LH2 complexes are designed to minimize the energetic disorder. We analyze quantitative implications of these for the energy transferring capability of the LH2 complex.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A Preprocessing Tool for Enhanced Ion Mobility–Mass Spectrometry-Based Omics Workflows

The ability to improve the data quality of ion mobility–mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Further, other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A First-Principles Approach to Modeling Surface Site Stabilities on Multimetallic Catalysts

The study of multimetallic alloys and the multitude of possible surface compositions have sparked a tremendous interest in engineering low-cost materials with high activity and selectivity in heterogeneous catalysis. Multimetallic systems provide complementary functionalities and an unprecedented tunability when designing catalyst formulations. However, due to their immense structural and compositional complexity, the investigation and identification of an optimal catalyst is a tedious and time-consuming process, both experimentally and theoretically. Therefore, theoretical design principles are highly desirable to accelerate the screening of catalyst structures across the vast compositional space. In this paper, we introduce a simple and general model for predicting the site stability of multimetallic surfaces and nanoparticles, which is based on physical principles. The model requires only a small set of density functional theory (DFT) calculations of metal atom binding energies on monometallic and dilute alloy surface slabs to optimize the parameters in the simple model. The resulting model allows for the quantification of the stability of any particular atom site in any conceivable chemical environment across a wide range of morphologies, sizes, and arrangements by interpolating the derived parameters from a monometallic system to a completely diluted alloyed system. Herein, we demonstrate the robustness of the model across an extensive data set of transition metal alloy surfaces and 147-atoms cuboctahedral nanoparticles (NPs) composed of IrRhRu and PtPdRu. In conclusion, our approach yields mean absolute errors of ≈0.15 (IrRhRu), 0.20 (PtPdRu), 0.19 (IrRhRu NP), and 0.26 (PtPdRu NP) eV relative to site binding energies calculated using DFT.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Synergistic Interactions During Co-Hydrothermal Liquefaction of Food Waste and Biomass Model Compounds for Increased Sustainable Aviation Fuel Production

Hydrothermal liquefaction (HTL) of lignocellulosic biomass is plagued with low biocrude yields owing to the tendency of highly reactive oxygenated intermediates to condense to form biochars. By contrast, the high protein content in food waste is comprised of substantial nitrogen species, which are known to interact strongly with oxygenates through Maillard, amide, and peptide bond formation reactions. Co-feeding food waste and lignocellulose opens new reaction pathways for biocrude formation but is currently poorly understood. This work evaluated the molecular level interactions between food waste and lignocellulose model compounds and the corresponding effect on product yields and quality. Food waste–cellulose and food waste–xylan feedstock blends achieved maximum biocrude carbon yield improvements of 12.2% and 10.1%, respectively, relative to a simple linear model that interpolates between the yields of the pure feedstocks. Increases in biocrude yield were balanced by corresponding decreases in char yield, indicating synergistic interactions between the feeds during HTL. Biocrude volatility analysis revealed that increased biocrude yield preferentially benefitted the jet fuel fraction, which comprised up to 22.6% of the total carbon yield for food waste–cellulose blends. Biocrude and char were analyzed using GC–MS and FT-IR spectroscopy to investigate the source of synergistic trends and provide greater mechanistic understanding. Key cofeeding effects included the promotion of retro-aldol condensation reactions and trans-esterification of fatty acids, sequestering carbon in the biocrude phase via the inhibition of char formation while increasing biocrude volatility toward jet fuel-range compounds. These results indicate the potential for judicious selection of HTL cofeeds to increase both biocrude yield and selectivity to desired fuel precursors, including sustainable aviation fuel.

Maillard reaction↗
DOE OSTI2026

Hourly PM 2.5 Estimates across California from 2018 to 2023

This study presents a new data set of hourly PM 2.5 concentrations across California from 2018 to 2023 at a three-kilometer resolution. This data set was developed by assimilating observations from PurpleAir and the U.S. EPA Air Quality System monitors into wildfire smoke forecasts from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model using the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework. Archived forecasts of modeled wildfire smoke PM 2.5 from HRRR-Smoke create the background field for assimilation, which is then corrected using surface observations of total PM 2.5 . The resulting reanalysis from GSI provides an estimate of total PM 2.5 that minimizes error from both the observational and the model data. Validation results indicate strong performance, with monthly R 2 values ranging from 0.73 to 0.91 across the six-year data set, comparable to other PM 2.5 data sets. Case studies are presented for three major fire events, the 2018 Camp Fire, 2019 Kincade Fire, and 2020 Lightning Complex Fires to demonstrate the data set’s fidelity in resolving plume dynamics and local exposure patterns. Root-mean-squared error averaged over each month scales with average PM 2.5 concentrations, resulting in a low error under typical conditions but higher absolute errors during extreme smoke events. This is the first long-term, hourly PM 2.5 data set of its kind for California and enables the generation of subdaily exposure metrics, such as peak hourly concentrations, exceedance durations, and time-of-day exposure peaks. The novelty and strong validation of this data set make it a compelling resource for future studies on the impact and significance of subdaily PM 2.5 exposure.

PM2.5↗
DOE OSTI2017

Implications of grain size variation in magnetic field alignment of block copolymer blends

Recent experiments have highlighted the intrinsic magnetic anisotropy in coil–coil diblock copolymers, specifically in poly(styrene- block -4-vinylpyridine) (PS- b -P4VP), that enables magnetic field alignment at field strengths of a few tesla. We consider here the alignment response of two low molecular weight (MW) lamallae-forming PS- b -P4VP systems. Cooling across the disorder–order transition temperature (T odt ) results in strong alignment for the higher MW sample (5.5K), whereas little alignment is discernible for the lower MW system (3.6K). This disparity under otherwise identical conditions of field strength and cooling rate suggests that different average grain sizes are produced during slow cooling of these materials, with larger grains formed in the higher MW material. Blending the block copolymers results in homogeneous samples which display T odt , d-spacings, and grain sizes that are intermediate between the two neat diblocks. Similarly, the alignment quality displays a smooth variation with the concentration of the higher MW diblock in the blends, and the size of grains likewise interpolates between limits set by the neat diblocks, with a factor of 3.5× difference in the grain size observed in high vs low MW neat diblocks. Finally, these results highlight the importance of grain growth kinetics in dictating the field response in block copolymers and suggests an unconventional route for the manipulation of such kinetics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Charge Hopping Dynamics along a Disordered Chain in Quantum Environments: Comparative Study of Different Rate Kernels

This work presents a computational study of charge hopping dynamics along a one-dimensional chain with Gaussian site energy disorder and linearly coupled quantum bath. Time dependent square displacements are calculated directly from numerical solutions of Pauli master equations, for five different rate kernels: exact Fermi golden rule (FGR) rate expression, stationary phase interpolation (SPI) approximation, semiclassical (SC) approximation, classical Marcus rate expression, and Miller-Abrahams expression. All results demonstrate diffusive behavior in the steady state limit. The results based on the FGR rate expression show that the charge transport in the quantum bath can be much more sensitive to the disorder than the prediction from the classical Marcus expression. While the SPI approximation captures this general trend reasonably well, the SC approximation tends to be unreliable at both quantitative and qualitative levels and becomes even worse than the classical Marcus expression under certain conditions. Furthermore, these results offer useful guidance in the choice of approximate rate kernels for larger-scale simulations and also demonstrate significant but fragile positive effects of quantum environments on the charge hopping dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Assessing the Skill of the Improved Treatment of Riverine Freshwater in the Community Earth System Model (CESM) Relative to a New Salinity Climatology

Abstract Recent studies have explored the sensitivity of global ocean model simulations to the treatment of riverine freshwater and the representation of estuarine processes via an estuary box model applied within Community Earth System Model (CESM). This study builds on these efforts by assessing the model skill score relative to a new salinity climatology. The new climatology averages the original observational data of the World Ocean Database directly onto the CESM ocean component tracer grid cells without spatial interpolation, smoothing, or other gap‐filling techniques to mitigate coastal ocean salinity bias present in the World Ocean Atlas. The mean square error for coastal upper ocean salinity relative to climatology is reduced by up to 14%, and the mean square error of near‐surface salinity stratification is reduced by up to 28% near major river mouths in the simulations with improved treatments of river runoff. The improvement in upper ocean bulk salinity is attributed primarily to focusing runoff as point sources thereby avoiding the artificial horizontal spreading of the control run and to applying a locally varying instead of a global constant reference salinity for riverine virtual salt fluxes. The improvements in near‐surface salinity stratification are primarily attributed to adding parameterized estuarine mixing with the estuary box model. Salinity and salinity stratification skill improvements are achieved not just near large rivers but also along the global coast and skill improvements extend far offshore. Despite these improvements, many other sources of model‐climatology mismatch in coastal salinity and stratification remain and merit further attention.

58 GEOSCIENCES↗
DOE OSTI2020

Simulating and Evaluating Atmospheric River-Induced Precipitation Extremes Along the U.S. Pacific Coast: Case Studies From 1980–2017

Atmospheric rivers (ARs) are responsible for a majority of extreme precipitation and floodevents along the U.S. West Coast. To better understand the present-day characteristics of AR-relatedprecipitation extremes, a selection of nine most intense historical AR events during 1980–2017 is simulatedusing a dynamical downscaling modeling framework based on the Weather Research and ForecastingModel. We find that the chosen framework and Weather Research and Forecasting Model configurationreproduces both large-scale atmospheric features—including parent synoptic-scale cyclones—as well asthe filamentary corridors of integrated vapor transport associated with the ARs themselves. The accuracyof simulated extreme precipitation maxima, relative to in situ and interpolated gridded observations,improves notably with increasing model resolution, with improvements as large as 40–60% for fine scale(3 km) relative to coarse-scale (27 km) simulations. A separate set of simulations using smoothedtopography suggests that much of these gains stem from the improved representation of complex terrain.Additionally, using the 12 December 1995 storm in Northern California as an example, we demonstratethat only the highest-resolution simulations resolve important fine-scale features—such as localizedorographically forced vertical motion and powerful near hurricane-force boundary layer winds. Given thedemonstrated ability of a targeted dynamical downscaling framework to capture both local extremeprecipitation and key fine-scale characteristics of the most intense ARs in the historical record, we arguethat such a configuration may be highly conducive to understanding AR-related extremes and associatedchanges in a warming climate

54 ENVIRONMENTAL SCIENCES↗