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Hierarchical Truncations for Many-Body Expansion Potentials
In this work, a new strategy to truncate high-order terms in the many-body expansion (MBE) is proposed. This new approach, which we call a hierarchical many-body expansion (HMBE), is based on a hierarchical partition of the system into multitier fragments and can in principle be applied to any large molecular system. Numerical tests on a series of (H 2 O) 64 structures are presented, demonstrating satisfactory relative energies between the clusters and binding energies of individual clusters compared with full-cluster calculations, with significantly fewer high-order terms computed than conventional MBE. The hierarchical truncation can be augmented by certain many-body terms for fragments at the interface between the partitions (called “Schengen terms”) to further improve accuracy. This work establishes the HMBE scheme as a promising framework to model very large systems (e.g., proteins), which are naturally built on a hierarchical structure.
Development of $\mathrm{AMOEBA}$ Polarizable Force Field for Rare-Earth La 3+ Interaction with Bioinspired Ligands
Rare-earth metals (REMs) are crucial for many important industries, such as power generation and storage, in addition to cancer treatment and medical imaging. One promising new REM refinement approach involves mimicking the highly selective and efficient binding of REMs observed in relatively recently discovered proteins. However, realizing any such bioinspired approach requires an understanding of the biological recognition mechanisms. In this report we developed a new classical polarizable force field based on the AMOEBA framework for modeling a lanthanum ion (La 3+ ) interacting with water, acetate, and acetamide, which have been found to coordinate the ion in proteins. The parameters were derived by comparing to high-level ab initio quantum mechanical (QM) calculations that include relativistic effects. The AMOEBA model, with advanced atomic multipoles and electronic polarization, is successful in capturing both the QM distance-dependent La 3+ –ligand interaction energies and experimental hydration free energy. A new scheme for pairwise polarization damping (POLPAIR) was developed to describe the polarization energy in La 3+ interactions with both charged and neutral ligands. Simulations of La3+ in water showed water coordination numbers and ion–water distances consistent with previous experimental and theoretical findings. Water residence time analysis revealed both fast and slow kinetics in water exchange around the ion. This new model will allow investigation of fully solvated lanthanum ion–protein systems using GPU-accelerated dynamics simulations to gain insights on binding selectivity, which may be applied to the design of synthetic analogues.
Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr 0.78 Y 0.22 O 3-δ
Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr 0.78 Y 0.22 O 3-δ using machine learning-accelerated ab initio molecular dynamics simulations. Here, our results reproduce the experimentally observed non-monotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration—two mechanisms that influence the lattice expansion in opposite directions. The importance of this work lies in its detailed demonstration of how advanced computational techniques can accurately capture complex environmental effects, providing a valuable framework for modeling similar phenomena in a variety of material systems and applications.
Colloidal Stability of PFSA-Ionomer Dispersions Part II: Determination of Suspension pH Using Single-Ion Potential Energies
Perfluorosulfonic acid (PFSA) ionomers serve a vital role in the performance and stability of fuel-cell catalyst layers. These properties, in turn, depend on the colloidal processing of precursor inks. Here, to understand the colloidal structure of fuel-cell catalyst layers, we explore the aggregation of PFSA ionomers dissolved in water/alcohol solutions and relate the predicted aggregation to experimental measurements of solution pH. Not all side chains contribute to measured pH because of burying inside particle aggregates. To account for the measured degree of dissociation, a new description is developed for how PFSA aggregates interact with each other. The developed single-counterion electrostatic repulsive pair potential from Part I is incorporated into the Smoluchowski collision-based kinetics of interacting aggregates with buried side chains. We demonstrate that the surrounding solvent mixture affects the degree of aggregation as well as the pH of the system primarily through the solution dielectric permittivity, which drives the strength of the interparticle repulsive energies. Successful pH prediction of Nafion ionomer dispersions in water/n-propanol solutions validates the numerical calculations. Nafion-dispersion pH measurements serve as a surrogate for Nafion particle-size distributions. The model and framework can be leveraged to explore different ink formulations.
Impact of Photon Recycling, Grain Boundaries, and Nonlinear Recombination on Energy Transport in Semiconductors
Energy carrier transport and recombination in semiconductors can be directly monitored with optical microscopy, revealing key insights into fundamental photophysics and informs efficient optoelectronic device design. Oftentimes, energy transport is measured by monitoring a time-resolved expanding carrier profile after optical excitation under a focused laser beam. Although this approach has gained widespread adoption, the presence of competing dynamic processes, such as diffusion and photon recycling, has made it difficult to accurately extract and understand important semiconductor properties. Here, we develop and apply a new framework for modeling energy transport in both excitonic and free carrier semiconductors. Here, we demonstrate that processes such as nonlinear recombination and photon recycling can have a significant impact on the apparent energy carrier profiles, especially for excitonic materials with short radiative lifetimes. Additionally, we find that film microstructure can lead to unique transport profiles that strongly depends on material boundary behavior and the difference between feature size and diffusion length. Here, these findings provide a deeper understanding of energy transport in semiconducting materials and provide new strategies for the design and optimization of electronic and optoelectronic devices.
Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning
Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small-scale features (e.g., microcracks in shales) as an effective medium. Although this fracture-matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, e.g., microcrack clogging during mineral reactions. Here, we employ deep learning in place of effective medium theory to upscale physical processes in small-scale features. Specifically, we consider reactive transport in a fracture-microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which the microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is firstly employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.
Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems
Abstract Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.
Ecosystem Water‐Saving Timescale Varies Spatially With Typical Drydown Length
Abstract Stomatal optimization theory is a commonly used framework for modeling how plants regulate transpiration in response to the environment. Most stomatal optimization models assume that plants instantaneously optimize a reward function such as carbon gain. However, plants are expected to optimize over longer timescales given the rapid environmental variability they encounter. There are currently no observational constraints on these timescales. Here, a new stomatal model is developed and is used to analyze the timescales over which stomatal closure is optimized. The proposed model assumes plants maximize carbon gain subject to the constraint that they cannot draw down soil moisture below a critical value. The reward is integrated over time, after being weighted by a discount factor that represents the timescale ( τ ) that a plant considers when optimizing stomatal conductance to save water. The model is simple enough to be analytically solvable, which allows the value of τ to be inferred from observations of stomatal behavior under known environmental conditions. The model is fitted to eddy covariance data in a range of ecosystems, finding the value of τ that best predicts the dynamics of evapotranspiration at each site. Across 82 sites, the climate metrics with the strongest correlation to τ are measures of the average number of dry days between rainfall events. Values of τ are similar in magnitude to the longest such dry period encountered in an average year. The results here shed light on which climate characteristics shape spatial variations in ecosystem‐level water use strategy.
The Fragility of Bedform‐Induced Hyporheic Zones: Exploring Impacts of Dynamic Groundwater Table Fluctuations
Abstract Hyporheic zones are commonly regarded as resilient and enduring interfaces between groundwater and surface water in river corridors. In particular, bedform‐induced advective pumping hyporheic exchange (bedform‐induced exchange) is often perceived as a relatively persistent mechanism in natural river systems driving water, solutes, and energy exchanges between the channel and its surrounding streambed sediments. Numerous studies have been based on this presumption. To evaluate the persistence of hyporheic zones under varying hydrologic conditions, we use a multi‐physics framework to model advective pumping bedform‐induced hyporheic exchange in response to a series of seasonal‐ and event‐scale groundwater table fluctuation scenarios, which lead to episodic river‐aquifer disconnections and reconnections. Our results suggest that hyporheic exchange is not as ubiquitous as generally assumed. Instead, the bedform‐induced hyporheic exchange is restricted to a narrow range of conditions characterized by minor river‐groundwater head differences, is intermittent, and can be easily obliterated by minor losing groundwater conditions. These findings shed light on the fragility of bedform‐induced hyporheic exchange and have important implications for biogeochemical transformations along river corridors.
Ineffectiveness of formamidine in suppressing ultralow thermal conductivity in cubic hybrid perovskite FAPbI 3
Understanding lattice dynamics and thermal transport mechanisms in cubic hybrid organic–inorganic perovskites remain challenging due to strong anharmonicity and phase transitions. Here, we investigate the thermal transport behavior in benchmark cubic hybrid perovskite FAPbI 3 by coupling first principles-based anharmonic lattice dynamics with a linearized Wigner transport equation. Using the Temperature-Dependent Effective Potential (TDEP) method, we stabilize the negative soft modes, primarily dominated by organic FA + cations. Our calculations predict an ultra-low thermal conductivity of ~$0.63\,{\rm{W}}{{\rm{m}}}^{-1}{{\rm{K}}}^{-1}$ at 300 K, following a temperature dependence of T −0.740 . Contrary to common assumptions, we find that the [PbI 3 ] 1- units, rather than FA + cations, dominate thermal resistance. Furthermore, we demonstrate that anharmonic force constants are highly temperature-sensitive, relying on 0-K force constants significantly underestimates thermal conductivity. Our study not only elucidates the microscopic mechanisms governing thermal transport in FAPbI 3 but also provides a robust framework for modeling heat conduction in hybrid organic-inorganic compounds.
Predicting the influence of particle size on the glass transition temperature and viscosity of secondary organic material
Abstract Atmospheric aerosols can assume liquid, amorphous semi-solid or glassy, and crystalline phase states. Particle phase state plays a critical role in understanding and predicting aerosol impacts on human health, visibility, cloud formation, and climate. Melting point depression increases with decreasing particle diameter and is predicted by the Gibbs–Thompson relationship. This work reviews existing data on the melting point depression to constrain a simple parameterization of the process. The parameter $$\xi $$ ξ describes the degree to which particle size lowers the melting point and is found to vary between 300 and 1800 K nm for a wide range of particle compositions. The parameterization is used together with existing frameworks for modeling the temperature and RH dependence of viscosity to predict the influence of particle size on the glass transition temperature and viscosity of secondary organic aerosol formed from the oxidation of $$\alpha $$ α -pinene. Literature data are broadly consistent with the predictions. The model predicts a sharp decrease in viscosity for particles less than 100 nm in diameter. It is computationally efficient and suitable for inclusion in models to evaluate the potential influence of the phase change on atmospheric processes. New experimental data of the size-dependence of particle viscosity for atmospheric aerosol mimics are needed to thoroughly validate the predictions.
Challenges and alternatives to empirical orthogonal functions for earth system data
Empirical orthogonal functions (EOFs) applied to gridded Earth system data enables users to diagnose modes of variability with relative ease. Yet, many challenges to interpretation exist such that they must be used with awareness and intention when applied to gridded climate data, especially with large ensembles. Utilizing data from two different Earth system modelling large ensemble frameworks, the Energy Exoscale Earth System Model and the Community Earth System Model, as well as reanalysis data, common EOF pitfalls are summarized and discussed. Challenges include erroneous mode swapping, sign flipping, and the temporal variability of the centers of action. For modes of variability with similar contribution to variance, mode swapping is not uncommon. Sign flipping can occur with almost any mode where the pattern is correct, but the sign is arbitrary. Although the variability of the center of action is not necessarily problematic, it potentially complicates interpretation over multi-century timescales. A wide variety of alternative methods to EOFs exist, but fitness-for-purpose must be evaluated. Additionally, illustrations of alternative methods and examples of proper use are provided. Alternative methods fit into three categories: EOF variants, linear methods, and multilinear methods.
BeyondPlanck: XV. Limits on large-scale polarized anomalous microwave emission from Planck LFI and WMAP
We constrained the level of polarized anomalous microwave emission (AME) on large angular scales using Planck Low-Frequency Instrument (LFI) and WMAP polarization data within a Bayesian cosmic microwave background (CMB) analysis framework. We modeled synchrotron emission with a power-law spectral energy distribution, as well as the sum of AME and thermal dust emission through linear regression with the Planck High-Frequency Instrument (HFI) 353 GHz data. This template-based dust emission model allowed us to constrain the level of polarized AME while making minimal assumptions on its frequency dependence. We neglected CMB fluctuations, but show through simulations that these fluctuations have a minor impact on the results. We find that the resulting AME polarization fraction confidence limit is sensitive to the polarized synchrotron spectral index prior. In addition, for prior means β s < -3.1 we find an upper limit of p AME max ≲ 0.6% (95% confidence). In contrast, for means β s = -3.0, we find a nominal detection of p AME = 2.5 ± 1.0% (95% confidence). These data are thus not strong enough to simultaneously and robustly constrain both polarized synchrotron emission and AME, and our main result is therefore a constraint on the AME polarization fraction explicitly as a function of β s . Combining the current Planck and WMAP observations with measurements from high-sensitivity low-frequency experiments such as C-BASS and QUIJOTE will be critical to improve these limits further.
Discrepancy in the Critical State Void Ratio of Poorly Graded Sand due to Shear Strain Localization
The critical state (CS) concept is a theoretical framework that models the constitutive behavior of soils, including sand and other granular materials. It supports the notion of a unique postfailure state, where the soil ultimately experiences continuous shearing with no change in the plastic volumetric strain. However, the published literature has frequently noted the nonconvergence of sand specimens with different initial densities to a unique CS in the compression plane due to many factors such as specimen fabric, particle morphology, breakage, and grain size distribution. This paper examines the CS for poorly graded (uniform) glass beads and 3 different types of silica sands using 50 conventional triaxial compression (CTC) experiments, 12 oedometer tests, and in situ synchrotron microcomputed tomography (SMT) scans for 10 CTC experiments. The results of the 50 CTC experiments revealed a diffused CS zone in the compression plane, which was further examined using the in situ SMT scans. A thorough three-dimensional image analysis of the SMT scans accurately quantified the evolution of the local void ratio (elocal) versus axial compression within zones of intensive shearing toward the center of the specimen. The evolution of the void ratio was also measured using the entire volume of the specimen (eglobal). At the CS, the elocal/eglobal ratio was assessed to be ~1.25 when a single shear band developed within the scanned specimens and ~1.1-1.15 for specimens that failed via external bulging that was internally manifested by the development of multiple shear bands. This finding suggests that the CS zone in the compression plane can be attributed to the common wrong consideration of eglobal evolution in lieu of elocal within the developing shear bands. Furthermore, the lack of shear band development in uniaxial compression has made the results of the oedometer test reliable in quantifying the CS parameters in the compression plane.
Resonant ultrasound spectroscopy for crystalline samples containing initial strain
Resonant ultrasound spectroscopy (RUS) is a mature and robust technique for the nondestructive characterization of the elastic properties of solids capable of providing the elastic constants of anisotropic crystalline solids. The traditional method is based on assuming that the solid is linear elastic and, therefore, obeys a linearized Hookean constitutive relationship (Hooke’s law). In this work, an alternative constitutive law is provided that allows for an initial strain or prestrain to be present stemming from residual stress. Then, the constitutive relationship is integrated into the RUS framework. The model is demonstrated using a realistic prestrain field obtained by simulating shot-peening processing of a polycrystalline Cu sample. The sensitivity of the resonances to the developed prestrain is established and discussed. This work allows researchers to consider the influence of initial strain or residual stress in their samples and the potential influence on accurate elastic constant estimates. The model also supports the potential of RUS for the nondestructive characterization of prestrain in materials.
Robust estimation of charge carrier diffusivity using transient photoluminescence microscopy
Transient microscopy has emerged as a powerful tool for imaging the diffusion of excitons and free charge carriers in optoelectronic materials. In many excitonic materials, extraction of diffusion coefficients can be simplified because of the linear relationship between signal intensity and local excited state population. However, in materials where transport is dominated by free charge carriers, extracting diffusivities accurately from multidimensional data is complicated by the nonlinear dependence of the measured signal on the local charge carrier density. To obtain accurate estimates of charge carrier diffusivity from transient microscopy data, statistically robust fitting algorithms coupled to efficient 3D numerical solvers that faithfully relate local carrier dynamics to raw experimental measurables are sometimes needed. Here, we provide a detailed numerical framework for modeling the spatiotemporal dynamics of free charge carriers in bulk semiconductors with significant solving speed reduction and for simulating the corresponding transient photoluminescence microscopy data. To demonstrate the utility of this approach, we apply a fitting algorithm using a Markov chain Monte Carlo sampler to experimental data on bulk CdS and methylammonium lead bromide (MAPbBr 3 ) crystals. Parameter analyses reveal that transient photoluminescence microscopy can be used to obtain robust estimates of charge carrier diffusivities in optoelectronic materials of interest, but that other experimental approaches should be used for obtaining carrier recombination constants. Additionally, simplifications can be made to the fitting model depending on the experimental conditions and material systems studied. Our open-source simulation code and fitting algorithm are made freely available to the scientific community.
Multi-site reaction dynamics through multi-fragment density matrix embedding
The practical description of disordered chemical reactions, where the reactions involve multiple species at multiple sites, is presently a challenge using correlated electronic structure methods due to their high computational cost and steep scaling. Here, we describe the gradient theory of multi-fragment density matrix embedding theory, which potentially provides a minimal computational framework to model such processes at the correlated electron level. We present the derivation and implementation of the gradient theory, its validation on model systems and chemical reactions using density matrix embedding, and its application to a molecular dynamics simulation of proton transport in a small water cluster, a simple example of multi-site reaction dynamics.