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DOE OSTI2025

DEMOS (Demographic Microsimulator Tool for Longitudinal Synthetic Population) [SWR-25-135] related to NLR SWR-26-076

The Demographic Microsimulator (DEMOS) is an agent-based simulation framework used to model the evolution of population demographic characteristics and lifecycle events, such as education attainment, marital status, and other key transitions. DEMOS modules are designed to capture the interdependencies between short-term and long-term lifecycle events, which are often influential in downstream transportation and land-use modeling. A key feature of DEMOS is its ability to track changes in an agent’s demographic status from year t to year t + 1. This structure allows the model to evolve populations over any user-defined time horizon. As a result, DEMOS is well suited for analyzing medium- and long-term transportation-related decisions, including household vehicle transactions (e.g., purchasing, selling, or replacing vehicles) and work location choices. Core features of DEMOS include the modeling of more than ten lifecycle events, behaviorally realistic patterns informed by long-running panel data, explicit representation of interdependencies among lifecycle processes, and a flexible, modular simulation architecture. A technical memorandum describing DEMOS is available here. The memorandum provides an overview of the framework’s functionality, model structure, input and output data, and its applications in transportation planning and broader policy analysis contexts. Interested readers are also encouraged to consult the paper listed below for additional details on the DEMOS methodology. Sun, Bingrong, Shivam Sharda, Venu M. Garikapati, Mohamed Amine Bouzaghrane, Juan Caicedo, Srinath Ravulaparthy, Isabel Viegas de Lima, Ling Jin, C. Anna Spurlock, and Paul Waddell. "Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life." Transportation Research Record (2025): 03611981251333339.

Sun, Bingrong [National Laboratory of the Rockies ↗
DOE OSTI2020

CF + excitation in the interstellar medium

The detection of CF + in interstellar clouds potentially allows astronomers to infer the elemental fluorine abundance and the ionization fraction in ultraviolet-illuminated molecular gas. Because local thermodynamic equilibrium (LTE) conditions are hardly fulfilled in the interstellar medium (ISM), the accurate determination of the CF + abundance requires one to model its non-LTE excitation via both radiative and collisional processes. Here, we report quantum calculations of rate coefficients for the rotational excitation of CF + in collisions with para- and ortho-H 2 (for temperatures up to 150 K). As an application, we present non-LTE excitation models that reveal population inversion in physical conditions typical of ISM photodissociation regions (PDRs). We successfully applied these models to fit the CF + emission lines previously observed toward the Orion Bar and Horsehead PDRs. The radiative transfer models achieved with these new rate coefficients allow the use of CF + as a powerful probe to study molecular clouds exposed to strong stellar radiation fields.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

AAPM Truth‐based CT (TrueCT) reconstruction grand challenge

Background: This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction. Purpose: To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases. Methods: Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1–6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9 mm for lung lesions and 3.9 to 14.9 mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82 ± 12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781 ± 11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were −950 ± 17 HU ranging from −918 to −979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. Further, the reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d’] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with “0” and “1” being the worst and best measured values across all cases of the disease type for all received reconstructions. Results: The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d’ from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1 mm3. The overall scores demonstrated that participant “A” had the best performance in all categories, except for the metrics of d’ for lung lesions and RMSE for liver lesions. Participant “A” had an average normalized score of 0.41 ± 0.22, 0.48 ± 0.32, and 0.42 ± 0.33 for the emphysema, lung lesion, and liver lesion cases, respectively. Conclusions: The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data

Abstract The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

Soni, Vivak (ORCID:0000000294969562)↗
DOE OSTI2022

Improving Predictive Capability in REHEDS Simulations with Fast, Accurate, and Consistent Non-Equilibrium Material Properties

Predictive design of REHEDS experiments with radiation-hydrodynamic simulations requires knowledge of material properties (e.g. equations of state (EOS), transport coefficients, and radiation physics). Interpreting experimental results requires accurate models of diagnostic observables (e.g. detailed emission, absorption, and scattering spectra). In conditions of Local Thermodynamic Equilibrium (LTE), these material properties and observables can be pre-computed with relatively high accuracy and subsequently tabulated on simple temperature-density grids for fast look-up by simulations. When radiation and electron temperatures fall out of equilibrium, however, non-LTE effects can profoundly change material properties and diagnostic signatures. Accurately and efficiently incorporating these non-LTE effects has been a longstanding challenge for simulations. At present, most simulations include non-LTE effects by invoking highly simplified inline models. These inline non-LTE models are both much slower than table look-up and significantly less accurate than the detailed models used to populate LTE tables and diagnose experimental data through post-processing or inversion. Because inline non-LTE models are slow, designers avoid them whenever possible, which leads to known inaccuracies from using tabular LTE. Because inline models are simple, they are inconsistent with tabular data from detailed models, leading to ill-known inaccuracies, and they cannot generate detailed synthetic diagnostics suitable for direct comparisons with experimental data. This project addresses the challenge of generating and utilizing efficient, accurate, and consistent non-equilibrium material data along three complementary but relatively independent research lines. First, we have developed a relatively fast and accurate non-LTE average-atom model based on density functional theory (DFT) that provides a complete set of EOS, transport, and radiative data, and have rigorously tested it against more sophisticated first-principles multi-atom DFT models, including time-dependent DFT. Next, we have developed a tabular scheme and interpolation methods that compactly capture non-LTE effects for use in simulations and have implemented these tables in the GORGON magneto-hydrodynamic (MHD) code. Finally, we have developed post-processing tools that use detailed tabulated non-LTE data to directly predict experimental observables from simulation output.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Integrated modeling methodology for ash agglomeration in poly-disperse fluidized beds using particle population framework

This article discusses a unique agglomeration modeling methodology developed based on binary collisions to combine the effects of heterogeneity in ash chemistry and granular physics. A simple population balance is defined to find changes in the particle size distribution (PSD) of a fluidized bed. Thermodynamic equilibrium calculations and a computational fluid dynamics (CFD) code, are used to obtain hydrodynamic parameters. A method to calculate and use a distribution of collision frequencies in fluidized beds, corresponding to a poly-disperse particle size distribution, was developed in order to incorporate the particle level heterogeneities. A distribution of collision frequencies obtained for poly-dispersed particles showed a three orders of magnitude higher collision frequency amongst the smaller particles than the coarser ones, at the initiation of agglomeration. Ash agglomeration occurred when the slag amount (binder) was less than 10 wt% at temperatures <850 °C for Pittsburgh No. 8 coal.

42 ENGINEERING↗
DOE OSTI2025

Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees

There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, infer genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and common-garden experiments to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and inform gene resource management. We analyzed population structure by partitioning phenotypic and genomic (single-nucleotide polymorphism) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60 to 81%) occurred among populations and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive abilityr> 0.9) but almost as well using climate or geography (r> 0.8). In contrast, genomic prediction within populations was poor (r< 0.2). We identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Given the urgent need to conserve natural populations and ecosystems, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration.

Science & Technology - Other Topics↗
DOE OSTI2024

Modelling the ejection of primary aerosols during the fast pyrolysis of biomass anisotropic particles

A model for the fast pyrolysis of anisotropic biomass particles is presented which considers bubbling dynamics within the liquid intermediate phase (metaplast) and aerosol ejection from this phase. The model employs the population balance equation and the method of moments to estimate the production rate and resultant size distribution of aerosol ejections, incorporating a detailed CRECK reaction mechanism, and considers the effect of anisotropic biomass microstructure on the intraparticle transport of mass and energy. Here, this study investigates the impact of particle size, heating rate (heat transfer coefficient), and lignocellulosic composition on aerosol ejection. The model predicts that, at high heating rates (convective heat transfer coefficient of 359 W/m 2 .K), aerosols can contribute over 20% to the heavy fraction yield in bio-oil for small particles (1 mm diameter, 4 mm length). The model can predict aerosol size distribution and surface area, indicating an average size of 20 μm for bubbles and 5 μm for aerosols during increased bubble production and aerosol ejection rates. These findings are consistent with prior experimental results and provide essential information for future modeling of extra-particle reactions of the aerosols as they progress through the reactor.

09 BIOMASS FUELS↗
DOE OSTI2022

Impact of Variable Gas Mixtures on Bubble Size Distribution and Mass Transfer in Gas Fermentation Reactors

Gas fermentation has emerged as a promising new technology for the generation of fuels and chemicals from mixtures of greenhouse and energy rich gas streams (CO2/CH4/H2/CO) via microbial bioreaction. Example pathways include biomethanation (CO2/H2 to CH4), biogas upgrading, CO fermentation and wet-waste conversion. Effective Gas-liquid mass-transfer is an important physical phenomenon that determines the design and scale-up of these systems. There is currently a knowledge-gap regarding bubble-size distributions when using a mixture of gases with vastly different properties, which can have a significant impact on overall mass-transfer. For example, hydrogen bubbles are more buoyant compared to other relatively heavier gases (CO2/CH4/CO), resulting in a large distribution of residence times and bubble sizes. This work therefore develops a deeper understanding of bubble dynamics and interphase mass transfer in such heterogenous gas mixtures through well-resolved computational models. We use a detailed multiphase computational-fluid-dynamics (CFD) model to study the impact of gas-mixtures on overall mass-transfer in bubble column and air-lift reactors. The CFD tool previously developed by the authors (1) for simulating aerobic fermentation reactors at scale is used in this study. The Reynolds-averaged mass, momentum, energy, and species transport equations are solved for interpenetrating gas and liquid phase in this model. We use a population balance-based bubble-size-distribution model that is validated against small-scale experiments in our solver. Results pertaining to multiple simulations of gas-fermentation reactors are presented where gas mixtures with varying compositions of CO2/CH4/CO/H2 are imposed at the sparger boundaries. The spatio-temporal variations in bubble-size distribution and mass transfer coefficient is analyzed for varying superficial velocities and gas-compositions for varying sizes of bubble-column and airlift reactors.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Statistical estimates of the binary properties of rotational variables

ABSTRACT We present a model to estimate the average primary masses, companion mass ranges, the inclination limit for recognizing a rotational variable, and the primary mass spreads for populations of binary stars. The model fits a population’s binary mass function distribution and allows for a probability that some mass functions are incorrectly estimated. Using tests with synthetic data, we assess the model’s sensitivity to each parameter, finding that we are most sensitive to the average primary mass and the minimum companion mass, with less sensitivity to the inclination limit and little to no sensitivity to the primary mass spread. We apply the model to five populations of binary spotted rotational variables identified in ASAS-SN, computing their binary mass functions using RV data from APOGEE. Their average primary mass estimates are consistent with our expectations based on their CMD locations ($\sim 0.75 \, {\rm M}_{\odot }$ for lower main sequence primaries and $\sim 0.9$–$1.2 \, {\rm M}_{\odot }$ for RS CVn and sub-subgiants). Their companion mass range estimates allow companion masses down to $M_2/M_1\simeq 0.1$, although the main sequence population may have a higher minimum mass fraction ($\sim 0.4$). We see weak evidence of an inclination limit $\gtrsim 50^{\circ }$ for the main sequence and sub-subgiant groups and no evidence of an inclination limit in the other groups. No groups show strong evidence for a preferred primary mass spread. We conclude by demonstrating that the approach will provide significantly better estimates of the primary mass and the minimum mass ratio and reasonable sensitivity to the inclination limit with 10 times as many systems.

Phillips, Anya (ORCID:000900051914974X)↗
DOE OSTI2020

Experimental evolution reveals nitrate tolerance mechanisms in Desulfovibrio vulgaris

Elevated nitrate in the environment inhibits sulfate reduction by important microorganisms of sulfate-reducing bacteria (SRB). Several SRB may respire nitrate to survive under elevated nitrate, but how SRB that lack nitrate reductase survive to elevated nitrate remains elusive. To understand nitrate adaptation mechanisms, we evolved 12 populations of a model SRB (i.e., Desulfovibrio vulgaris Hildenborough, DvH) under elevated NaNO 3 for 1000 generations, analyzed growth and acquired mutations, and linked their genotypes with phenotypes. Nitrate-evolved (EN) populations significantly (p < 0.05) increased nitrate tolerance, and whole-genome resequencing identified 119 new mutations in 44 genes of 12 EN populations, among which six functional gene groups were discovered with high mutation frequencies at the population level. In this work, we observed a high frequency of nonsense or frameshift mutations in nitrosative stress response genes (NSR: DVU2543, DVU2547, and DVU2548), nitrogen regulatory protein C family genes (NRC: DVU2394-2396, DVU2402, and DVU2405), and nitrate cluster (DVU0246-0249 and DVU0251). Mutagenesis analysis confirmed that loss-of-functions of NRC and NSR increased nitrate tolerance. Also, functional gene groups involved in fatty acid synthesis, iron regulation, and two-component system (LytR/LytS) known to be responsive to multiple stresses, had a high frequency of missense mutations. Additionally, mutations in those gene groups could increase nitrate tolerance through regulating energy metabolism, barring entry of nitrate into cells, altering cell membrane characteristics, or conferring growth advantages at the stationary phase. This study advances our understanding of nitrate tolerance mechanisms and has important implications for linking genotypes with phenotypes in DvH.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Simulation Models for Exploring Magnetic Reconnection

Simulations have played a critical role in the advancement of our knowledge of magnetic reconnection. However, due to the inherently multiscale nature of reconnection, it is impossible to simulate all physics at all scales. For this reason, a wide range of simulation methods have been crafted to study particular aspects and consequences of magnetic reconnection. This article reviews many of these methods, laying out critical assumptions, numerical techniques, and giving examples of scientific results. Plasma models described include magnetohydrodynamics (MHD), Hall MHD, Hybrid, kinetic particle-in-cell (PIC), kinetic Vlasov, Fluid models with embedded PIC, Fluid models with direct feedback from energetic populations, and the Rice Convection Model (RCM).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Vaccination compartmental epidemiological models for the delta and omicron SARS-CoV-2 variants

We explore the inclusion of vaccination in compartmental epidemiological models concerning the delta and omicron variants of the SARS-CoV-2 virus that caused the COVID-19 pandemic. We expand on our earlier compartmental-model work by incorporating vaccinated populations. We present two classes of models that differ depending on the immunological properties of the variant. The first one is for the delta variant, where we do not follow the dynamics of the vaccinated individuals since infections of vaccinated individuals were rare. The second one for the far more contagious omicron variant incorporates the evolution of the infections within the vaccinated cohort. We explore comparisons with available data involving two possible classes of counts, fatalities and hospitalizations. We present our results for two regions, Andalusia and Switzerland (including the Principality of Liechtenstein), where the necessary data are available. In the majority of the considered cases, the models are found to yield good agreement with the data and have a reasonable predictive capability beyond their training window, rendering them potentially useful tools for the interpretation of the COVID-19 and further pandemic waves, and for the design of intervention strategies during these waves.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Wolf-Rayet Galaxies in SDSS-IV MaNGA. II. Metallicity Dependence of the High-mass Slope of the Stellar Initial Mass Function

As hosts of living high-mass stars, Wolf-Rayet (WR) regions or WR galaxies are ideal objects for constraining the high-mass end of the stellar initial mass function (IMF). We construct a large sample of 910 WR galaxies/regions that cover a wide range of stellar metallicity (from Z ~ 0.001 to 0.03) by combining three catalogs of WR galaxies/regions previously selected from the SDSS and SDSS-IV/MaNGA surveys. We measure the equivalent widths of the WR blue bump at ~4650 Å for each spectrum. They are compared with predictions from stellar evolutionary models Starburst99 and BPASS, with different IMF assumptions (high-mass slope α of the IMF ranging from 1.0 to 3.3). Both singular evolution and binary evolution are considered. We also use a Bayesian inference code to perform full spectral fitting to WR spectra with stellar population spectra from BPASS as fitting templates. We then make a model selection among different α assumptions based on Bayesian evidence. These analyses have consistently led to a positive correlation of the IMF high-mass slope α with stellar metallicity Z, i.e., with a steeper IMF (more bottom-heavy) at higher metallicities. Specifically, an IMF with α = 1.00 is preferred at the lowest metallicity (Z ~ 0.001), and an Salpeter or even steeper IMF is preferred at the highest metallicity (Z ~ 0.03). These conclusions hold even when binary population models are adopted.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

SDSS-IV MaNGA: Bayesian analysis of the star formation history of low-mass galaxies in the local Universe

ABSTRACT We measure the star formation histories (SFHs) of a sample of low-mass galaxies with M* < 109 M⊙ from the SDSS-IV MaNGA survey. The large number of IFU spectra for each galaxy are either combined to reach a high signal to noise ratio or used to investigate spatial variations. We use Bayesian inferences based on full spectrum fitting. Our analysis based on Bayesian evidence ratio indicates a strong preference for a model that allows the presence of an old stellar population, and that an improper model for the SFH can significantly underestimate the old population in these galaxies. The addition of near-infrared photometry to the constraining data can further distinguish between different SFH model families and significantly tighten the constraints on the mass fraction in the old population. On average more than half of the stellar mass in present-day low-mass galaxies formed at least 8 Gyr ago, while about 30 per cent within the past 4 Gyr. Satellite galaxies on average have formed their stellar mass earlier than central galaxies. The radial dependence of the SFH is quite weak. Our results suggest that most of the low-mass galaxies have an early episode of active star formation that produces a large fraction of their present stellar mass.

Zhou, Shuang↗
DOE OSTI2024

Divergent urban land trajectories under alternative population projections within the Shared Socioeconomic Pathways

Population change is a main driver behind global environmental change, including urban land expansion. In future scenario modeling, assumptions regarding how populations will change locally, despite identical global constraints of Shared Socioeconomic Pathways (SSPs), can have dramatic effects on subsequent regional urbanization. Using a spatial modeling experiment at high resolution (1 km), this study compared how two alternative US population projections, varying in the spatially explicit nature of demographic patterns and migration, affect urban land dynamics simulated by the Spatially Explicit, Long-term, Empirical City development (SELECT) model for SSP2, SSP3, and SSP5. The population projections included: (1) newer downscaled state-specific population (SP) projections inclusive of updated international and domestic migration estimates, and (2) prevailing downscaled national-level projections (NP) agnostic to localized demographic processes. Our work shows that alternative population inputs, even those under the same SSP, can lead to dramatic and complex differences in urban land outcomes. Under the SP projection, urbanization displays more of an extensification pattern compared to the NP projection. This suggests that recent demographic information supports more extreme urban extensification and land pressures on existing rural areas in the US than previously anticipated. Urban land outcomes to population inputs were spatially variable where areas in close spatial proximity showed divergent patterns, reflective of the spatially complex urbanization processes that can be accommodated in SELECT. Although different population projections and assumptions led to divergent outcomes, urban land development is not a linear product of population change but the result of complex relationships between population, dynamic urbanization processes, stages of urban development maturity, and feedback mechanisms. These findings highlight the importance of accounting for spatial variations in the population projections, but also urbanization process to accurately project long-term urban land patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Determining Partial Atomic Charges for Liquid Water: Assessing Electronic Structure and Charge Models

Partial atomic charges provide an intuitive and efficient way to describe the charge distribution and the resulting intermolecular electrostatic interactions in liquid water. Many charge models exist and it is unclear which model provides the best assignment of partial atomic charges in response to the local molecular environment. In this work, we systematically scrutinize various electronic structure methods and charge models (Mulliken, natural population analysis, CHelpG, RESP, Hirshfeld, Iterative Hirshfeld, and Bader) by evaluating their performance in predicting the dipole moments of isolated water, water clusters, and liquid water as well as charge transfer in the water dimer and liquid water. Although none of the seven charge models is capable of fully capturing the dipole moment increase from isolated water (1.85 D) to liquid water (about 2.9 D), the Iterative Hirshfeld method performs best for liquid water, reproducing its experimental average molecular dipole moment, yielding a reasonable amount of intermolecular charge transfer, and showing modest sensitivity to the local water environment. The performance of the charge model is dependent on the choice of the density functional and the quantum treatment of the environment. The computed molecular dipole moment of water generally increases with the percentage of the exact Hartree–Fock exchange in the functional, whereas the amount of charge transfer between molecules decreases. For liquid water, including two full solvation shells of surrounding water molecules (within about 5.5 Å of the central water) in the quantum chemical calculation converges the charges of the central water molecule. Furthermore, our final pragmatic quantum chemical charge-assigning protocol for liquid water is the Iterative Hirshfeld method with M06-HF/aug-cc-pVDZ and a quantum region cutoff radius of 5.5 Å.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗