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Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors
Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.
Density split statistics: Joint model of counts and lensing in cells
We present density split statistics, a framework that studies lensing and counts-in-cells as a function of foreground galaxy density, thereby providing a large-scale measurement of both 2-point and 3-point statistics. Our method extends our earlier work on trough lensing and is summarized as follows: given a foreground (low redshift) population of galaxies, we divide the sky into subareas of equal size but distinct galaxy density. We then measure lensing around uniformly spaced points separately in each of these subareas, as well as counts-in-cells statistics (CiC). The lensing signals trace the matter density contrast around regions of fixed galaxy density. Through the CiC measurements this can be related to the density profile around regions of fixed matter density. Together, these measurements constitute a powerful probe of cosmology, the skewness of the density field and the connection of galaxies and matter. In this paper we show how to model both the density split lensing signal and CiC from basic ingredients: a non-linear power spectrum, clustering hierarchy coefficients from perturbation theory and a parametric model for galaxy bias and shot-noise. Furthermore, using N-body simulations, we demonstrate that this model is sufficiently accurate for a cosmological analysis on year 1 data from the Dark Energy Survey.
On discriminating tokamak sawtooth crash models via localized density and temperature measurements
The core electron temperature drops rapidly during the sawtooth crash in tokamak plasmas, which causes heat loss and may lead to fast particle losses or even a disruption. Several models have been proposed for the periodic crash, including the Kadomtsev model with magnetic reconnection and the quasi-interchange model with the growth of higher-mode-number pressure-driven instabilities. 3D MHD simulations were performed for these two models with a goal to develop intuition and to predict qualitatively how different types of sawtooth will appear in various diagnostics. The structures of electron density ne and electron temperature Te show a dominant (1, 1) mode for the Kadomtsev case and a dominant (4, 4) mode for the quasi-interchange case. The oscillations of ne and Te have a positive correlation near the inversion layer for both cases, while their frequencies and amplitudes are different depending on the dominant modes. Particularly, for the Kadomtsev case, we find a relation between the amount of flux reconnected during a sawtooth event and ne or Te oscillations. Therefore, we connect recently developed measurement capabilities for ne and Te to the internal sawtooth behavior. We propose that this method of analysis can help in identifying the type of sawtooth in future experiments augmented by simulations.
Performance of density functionals for modeling vapor liquid equilibria of CO 2 and SO 2
Vapor liquid equilibria (VLE) and condensed phase properties of carbon dioxide and sulfur dioxide are calculated using first principles Monte Carlo (FPMC) simulations to assess the performance of several density functionals, notably PBE-D3, BLYP-D3,PBE0-D3, M062X-D3, and rVV10. GGA functionals were used to compute complete vapor liquid coexistence curves (VLCCs) to estimate critical properties, while the hybrid and nonlocal vander Waals functionals were used only for computing density at a single state point due to the high computational cost. Our results show that the BLYP-D3 functional performs well in predicting VLE properties for both molecules when compared with other functionals. In the liquid phase, pair correlation functions reveal that there is not a significant difference in the location of the peak for the first solvation shell while the peak heights are different for different functionals. Altogether, theBLYP-D3 functional is a good choice for modeling VLE of acidic gases with significant environmental implications such as CO 2 and SO 2 .
Water Lone Pair Delocalization in Classical and Quantum Descriptions of the Hydration of Model Ions
Understanding the nature of ionic hydration at a fundamental level has eluded scientists despite intense interest for nearly a century. In particular, the microscopic origins of the asymmetry of ion solvation thermodynamics with respect to the sign of the ionic charge remains a mystery. Here, we determine the response of accurate quantum mechanical water models to strong nanoscale solvation forces arising from excluded volumes and ionic electrostatic fields. This is compared to the predictions of two important limiting classes of classical models of water with fixed point changes, differing in their treatment of “lone pair” electrons. Using the quantum water model as our standard of accuracy, we find that a single fixed classical treatment of lone pair electrons cannot accurately describe solvation of both apolar and cationic solutes, emphasizing the need for a more flexible description of local electronic effects in solvation processes. However, we explicitly show that all water models studied respond to weak long-ranged electrostatic perturbations in a manner that follows macroscopic dielectric continuum models, as would be expected. Here, we emphasize the importance of these findings in the context of realistic ion models, using density functional theory and empirical models, and discuss the implications of our results for quantitatively accurate reduced descriptions of solvation in dielectric media.
Plating and Stripping of Lithium Metal Stabilized by a Block Copolymer Electrolyte: Local Current Density Measurement and Modeling
Here, plating and stripping of lithium protrusions in lithium metal symmetric cells containing a solid block copolymer electrolyte was studied as a function of time in 3D using time-resolved X-ray tomography. These measurements enabled determination of the spatial variation in current densities at the plating and stripping electrodes. The initial interelectrode distance was 27 μm. Correlation functions were calculated to reveal the relationships between current densities at the two electrodes and local electrolyte thickness. Current densities at opposing electrode locations during protrusion growth is uncorrelated until the local interelectrode distance decreases to less than 6 μm, just before the cell shorts. Mass balance was used to determine the area from which lithium ions that form a protrusion were stripped. Computational modeling of the plating and stripping process reveals the interplay between electrochemical and mechanical driving forces and their effect on nonuniform current distribution. Model predictions were compared with experiments without resorting to any adjustable parameters. The computed correlation functions were in qualitative agreement with experiments. Finally, the model was used to calculate contour plots of electrochemical potential within the electrolyte, shedding light on how geometry, salt concentration, interelectrode distance, and mechanical stress influence local rates of electrochemical reaction.
Detonability of white dwarf plasma: turbulence models at low densities
We investigate the conditions required to produce self-sustained detonations in turbulent, carbon–oxygen degenerate plasma at low densities. We perform a series of three-dimensional hydrodynamic simulations of turbulence driven with various degrees of compressibility. The average conditions in the simulations are representative of models of merging binary white dwarfs. We find that material with very short ignition times is abundant in case turbulence is driven compressively. This material forms contiguous structures that persist over many ignition times, and that we identify as prospective detonation kernels. Detailed analysis of prospective kernels reveals that these objects are centrally condensed and their shape is characterized by low curvature, supportive of self-sustained detonations. The key characteristic of the newly proposed detonation mechanism is thus high degree of compressibility of turbulent drive. The simulated detonation kernels have sizes notably smaller than the spatial resolution of any white dwarf merger simulation performed to date. The resolution needed to resolve kernels is 0.1 km. Our results indicate a high probability of detonations in such well-resolved simulations of carbon–oxygen white dwarf mergers. These simulations will likely produce detonations in systems of lower total mass, thus broadening the population of white dwarf binaries capable of producing Type Ia supernovae. Consequently, we expect a downward revision of the lower limit of the total merger mass that is capable of producing a prompt detonation. We review application of the new detonation mechanism to various explosion scenarios of single, Chandrasekhar-mass white dwarfs.
Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates
The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.
Estimation and Modeling of Enceladus Plume Jet Density Using Reaction Wheel Control Data
The Cassini spacecraft was launched on October 15, 1997 by a Titan 4B launch vehicle. After an interplanetary cruise of almost seven years, it arrived at Saturn on June 30, 2004. In 2005, Cassini completed three flybys of Enceladus, a small, icy satellite of Saturn. Observations made during these flybys confirmed the existence of a water vapor plume in the south polar region of Enceladus. Five additional low-altitude flybys of Enceladus were successfully executed in 2008-9 to better characterize these watery plumes. The first of these flybys was the 50-km Enceladus-3 (E3) flyby executed on March 12, 2008. During the E3 flyby, the spacecraft attitude was controlled by a set of three reaction wheels. During the flyby, multiple plume jets imparted disturbance torque on the spacecraft resulting in small but visible attitude control errors. Using the known and unique transfer function between the disturbance torque and the attitude control error, the collected attitude control error telemetry could be used to estimate the disturbance torque. The effectiveness of this methodology is confirmed using the E3 telemetry data. Given good estimates of spacecraft's projected area, center of pressure location, and spacecraft velocity, the time history of the Enceladus plume density is reconstructed accordingly. The 1 sigma uncertainty of the estimated density is 7.7%. Next, we modeled the density due to each plume jet as a function of both the radial and angular distances of the spacecraft from the plume source. We also conjecture that the total plume density experienced by the spacecraft is the sum of the component plume densities. By comparing the time history of the reconstructed E3 plume density with that predicted by the plume model, values of the plume model parameters are determined. Results obtained are compared with those determined by other Cassini science instruments.
Estimation and Modeling of Enceladus Plume Jet Density Using Reaction Wheel Control Data
The Cassini spacecraft was launched on October 15, 1997 by a Titan 4B launch vehicle. After an interplanetary cruise of almost seven years, it arrived at Saturn on June 30, 2004. In 2005, Cassini completed three flybys of Enceladus, a small, icy satellite of Saturn. Observations made during these flybys confirmed the existence of a water vapor plume in the south polar region of Enceladus. Five additional low-altitude flybys of Enceladus were successfully executed in 2008-9 to better characterize these watery plumes. The first of these flybys was the 50-km Enceladus-3 (E3) flyby executed on March 12, 2008. During the E3 flyby, the spacecraft attitude was controlled by a set of three reaction wheels. During the flyby, multiple plume jets imparted disturbance torque on the spacecraft resulting in small but visible attitude control errors. Using the known and unique transfer function between the disturbance torque and the attitude control error, the collected attitude control error telemetry could be used to estimate the disturbance torque. The effectiveness of this methodology is confirmed using the E3 telemetry data. Given good estimates of spacecraft's projected area, center of pressure location, and spacecraft velocity, the time history of the Enceladus plume density is reconstructed accordingly. The 1-sigma uncertainty of the estimated density is 7.7%. Next, we modeled the density due to each plume jet as a function of both the radial and angular distances of the spacecraft from the plume source. We also conjecture that the total plume density experienced by the spacecraft is the sum of the component plume densities. By comparing the time history of the reconstructed E3 plume density with that predicted by the plume model, values of the plume model parameters are determined. Results obtained are compared with those determined by other Cassini science instruments.
Shock-associated MHD waves - A model for interstellar density fluctuations
The possibility that the density fluctuations responsible for radio scintillations could be due to ion-beam-generated MHD waves near interstellar shock waves is discussed. This suggestion is inspired by spacecraft observations which reveal these phenomena near shocks in the solar system. The model quite naturally accounts for the scale on which these fluctuations occur; it is dictated by the wavelength of the unstable waves.
Interaction of rate- and size-effect using a dislocation density based strain gradient viscoplasticity model
Not Available
An ISEE/Whistler model of equatorial electron density in the magnetosphere
Attention is given to an empirical model of equatorial electron density in the magnetosphere covering the L range 2.25-8. Although the model is primarily intended for application to the local time interval 00-15 MLT, a way to extend the model to the 15-24-MLT period is presented. The model describes, in piecewise fashion, the 'saturated' plasmasphere, the region of steep plasmapause gradients, and the plasma trough. Within the plasmasphere the model profile can be expressed as logne - Sigma-xi, where x1 = -0.3145L + 3.9043 is the principal or 'reference' term, and additional terms account for: a solar cycle variation with a peak at solar maximum; an annual variation with a December maximum; and a semiannual variation with equinoctial maxima.
A fast and robust computational modeling approach for density and shape predictions in powder metallurgy hot isostatic pressing
Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that produces near-net-shape parts with high material utilization and uniform microstructures. PM-HIP is frequently used for producing small-scale parts with complicated geometries and is potentially economical for producing large-scale parts. However, excessive post-HIP shape distortions can reduce its effectiveness and economic advantage, especially for larger parts. A PM-HIP computational model can predict and help mitigate these distortions. However, due to complex deformation mechanisms and thermo-mechanical coupling present in PM-HIP processes, these non-linear computational models sometimes become numerically unstable. The numerical instabilities in these models can lead to very slow convergence or no convergence at all, which often translates to slow and unreliable models. These limitations are more pronounced in large models with complicated geometries. Hence, in this work, an alternative modeling approach is presented that improves numerical stability and computational performance. The presented approach achieves these improvements through approximating the fully coupled thermo-mechanical PM-HIP model as a decoupled model and adding inertial damping to the model’s mechanical part. In conclusion, a comparison with the fully coupled model indicated a slight dip in prediction accuracy (<5% error) but significant improvements in numerical stability (>20 times larger time step size) and computational performance (5-10 times speed-up with less computational resource usage) when using the presented approach.
Transforming Energy through Computational Excellence. Exascale Computing: Combustion; Deep Learning for Presumed Probability Density Function (PDF) Models
NREL researchers use advanced machine learning techniques to define improved methods using deep learning models to resolve reacting flows in turbulent combustion flows, reducing the computational burden, increasing computational speed, and improving accuracy. These advancements reduce cost and improve fidelity of rapid-turn-around engineering calculations.
A new model for active nucleation site density in boiling systems
A new model for active nucleation site density in boiling systems has been developed. The model is based on parametric analysis of existing experimental data and is a function of wall superheat, pressure, and contact angle. The model is applicable for a wide pressure range - from 0.101 MPa to 19.8 MPa. Preliminary studies of temperature dependency of the contact angle have also been performed. The new model is intended to improve the predictions of the interfacial area concentration in two-fluid models and the wall temperature in subcooled boiling simulations. Validation activities have shown that the active nucleation site density predicted by the new model is in a good agreement with various published experimental data, and have demonstrated the achieved improvements as compared to existing models. (authors)