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DOE OSTI2020

Climate Sensitivity and the Direct Effect of Carbon Dioxide in a Limited-Area Cloud-Resolving Model

Even in a small domain, it can be prohibitively expensive to run cloud-resolving greenhouse gas warming experiments due to the long equilibration time. Here, a technique is introduced that reduces the computational cost of these experiments by an order of magnitude: instead of fixing the carbon dioxide concentration and equilibrating the sea surface temperature (SST), this technique fixes the SST and equilibrates the carbon dioxide concentration. Using this approach in a cloud-resolving model of radiative–convective equilibrium (RCE), the equilibrated SST is obtained as a continuous function of carbon dioxide concentrations spanning 1 ppmv to nearly 10 000 ppmv, revealing a dramatic increase in equilibrium climate sensitivity (ECS) at higher temperatures. This increase in ECS is due to both an increase in forcing and a decrease in the feedback parameter. In addition, the technique is used to obtain the direct effects of carbon dioxide (i.e., the rapid adjustments) over a wide range of SSTs. Overall, the direct effect of carbon dioxide offsets a quarter of the increase in precipitation from warming, reduces the shallow cloud fraction by a small amount, and has no impact on convective available potential energy (CAPE).

Romps, David M.↗

DOE OSTI2021

Reliability‐based layout optimization in offshore wind energy systems

Abstract Existing methods for optimizing wind array layouts typically use power or cost objectives and rarely consider reliability‐based objectives. Component and system failure rates, however, are dependent on location‐specific wind conditions, are influenced by array layout and wake interactions, and have a direct and significant impact on capital costs, operational costs, and power production. Although wind power plant models exist that calculate wind loads with sufficient resolution to capture component loading dynamics from wind conditions, they are computationally expensive and thus not suitable for research applications requiring many evaluations, particularly optimization. This study describes the development of computationally efficient, reliability‐based layout optimization methods, enabling us to explore the relationship between component reliability and layout optimization. These methods include the surrogate modeling of the planet bearing life based on varying wind conditions simulated in FAST.Farm and the formulation of reliability‐based objectives based on failure cost and power production models. Through demonstration of this method, we explore how wind conditions, objective functions, and capacity density influence reliability‐based layout optimization. Results indicate that considering reliability alongside power production can reduce failure costs associated with replacement costs and downtime while maintaining or improving power production. Our conclusions highlight the opportunity for wind power plant developers to integrate reliability and operational expenditures alongside performance and capital expenditure objectives in plant design and development to improve plant performance and costs.

17 WIND ENERGY↗

DOE OSTI2026

Multiscale analysis of large twist ferroelectricity and swirling dislocations in bilayer hexagonal boron nitride

With its atomically thin structure and intrinsic ferroelectric properties, heterodeformed bilayer hexagonal boron nitride (hBN) has gained prominence in next-generation non-volatile memory applications. However, studies to date have focused almost exclusively on small-twist bilayer hBN, leaving the question of whether ferroelectricity can persist under small heterostrain and large heterodeformation entirely unexplored. In this work, we establish the crystallographic origin of ferroelectricity in bilayer hBN configurations heterodeformed relative to high-symmetry configurations such as AA-stacking and 21.786789° twisted configurations (Σ7), using Smith normal form bicrystallography. We then demonstrate out-of-plane ferroelectricity in bilayer hBN across configurations vicinal to both the AA and Σ7 stackings. Atomistic simulations reveal that AA-vicinal systems support ferroelectricity under both small twist and small strain, with polarization switching in the latter governed by the deformation of swirling dislocations rather than the straight interface dislocations seen in the former. For Σ7-vicinal systems, where existing interatomic potentials underperform particularly under extreme out-of-plane compression, we develop a density-functional-theory-informed continuum framework—the bicrystallography-informed frame-invariant multiscale (BFIM) model, which captures out-of-plane ferroelectricity in heterodeformed configurations vicinal to Σ7 stacking. Interface dislocations in these large heterodeformed bilayer configurations exhibit markedly smaller Burgers vectors compared to interface dislocations in small-twist and small-strain bilayer hBN. The BFIM model reproduces experimental results and provides a powerful, computationally efficient framework for predicting ferroelectricity in large-unit-cell heterostructures where atomistic simulations are prohibitively expensive.

Ahmed, Md Tusher [Univ. of Illinois at Urbana-Cham↗

DOE OSTI2021

CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps

Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. Here, we introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

High mass and halo resolution from fast low resolution simulations

Generating mocks for future sky surveys requires large volumes and high resolutions, which is computationally expensive even for fast simulations. Here we try to develop numerical schemes to calibrate various halo and matter statistics in fast low resolution simulations compared to high resolution N-body and hydrodynamic simulations. For the halos, we improve the initial condition resolution and develop a halo finder "relaxed-FoF", where we allow different linking lengths for different halo mass and velocity dispersions. We show that our relaxed-FoF halo finder improves the common statistics, such as halo bias, halo mass function, halo auto power spectrum, cross correlation coefficient with the reference halo catalog, and halo-matter cross power spectrum. We also calibrate small-scale velocities of small halos to improve the power spectrum in redshift space. For the matter statistics, we incorporate the potential gradient descent (PGD) method into fast simulations to improve the matter distribution at nonlinear scales. By building a lightcone output, we show that the PGD method significantly improves the weak lensing convergence tomographic power spectrum. With these improvements FastPM is comparable to the high resolution full N-body simulation of the same mass resolution, with two orders of magnitude fewer time steps. These techniques can be used to improve the halo and matter statistics of FastPM simulations for mock catalogs of future surveys such as DESI and LSST.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Efficient formulation of multitime generalized quantum master equations: Taming the cost of simulating 2D spectra

Modern 4-wave mixing spectroscopies are expensive to obtain experimentally and computationally. In certain cases, the unfavorable scaling of quantum dynamics problems can be improved using a generalized quantum master equation (GQME) approach. However, the inclusion of multiple (light–matter) interactions complicates the equation of motion and leads to seemingly unavoidable cubic scaling in time. In this paper, we present a formulation that greatly simplifies and reduces the computational cost of previous work that extended the GQME framework to treat arbitrary numbers of quantum measurements. Specifically, we remove the time derivatives of quantum correlation functions from the modified Mori–Nakajima–Zwanzig framework by switching to a discrete-convolution implementation inspired by the transfer tensor approach. We then demonstrate the method’s capabilities by simulating 2D electronic spectra for the excitation-energy-transfer dimer model. In our method, the resolution of data can be arbitrarily coarsened, especially along the t2 axis, which mirrors how the data are obtained experimentally. Even in a modest case, this demands O(103) fewer data points. We are further able to decompose the spectra into one-, two-, and three-time correlations, showing how and when the system enters a Markovian regime where further measurements are unnecessary to predict future spectra and the scaling becomes quadratic. This offers the ability to generate long-time spectra using only short-time data, enabling access to timescales previously beyond the reach of standard methodologies.

Chemistry↗

DOE OSTI2019

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

Within the DOE complex, 3013 canisters are used to store Pu-bearing waste. Wall penetration due to corrosion, specifically stress corrosion cracking, is considered to be the most likely cause of failure over the 50-year lifetime of the canisters. This project has 2 objectives: The first is the development of machine learning algorithms to identify the presence of corrosion from a very large set of images generated by laser confocal microscope scanning of 3013 canister used to store Pu oxides. This portion of the LDRD constitutes image analysis of a metal surface for the presence of corrosion. The image processing algorithms developed for this project will provide a suitable basis for analysis of other types of corrosion data, which can be produced in vast quantities using modern devices. The second component of the LDRD consists of the development of machine learning algorithms that obtain molecular mechanics force-fields from ab-initio Density Functional Theory (DFT) calculations for corrosive attack by chlorides on 304L or 316L stainless steel. The goal of this latter component is to determine means for mitigating corrosion on a fundamental level, including coatings, welding methods, metal composition, etc. Force-field modeling is necessary for this endeavor because the incipience and progression of corrosion is the governed by molecular structures, grain boundaries, dislocations and surface structures represented by large numbers of atoms. Although DFT calculations are extremely adept at describing molecular scale processes, they are computationally expensive making them ill-suited for calculations of more than several hundred atoms, especially for the repeated applications inherent in material design. Fortunately, force-field methods provide an avenue for viable molecular-scale calculations involving the numbers of atoms involved in corrosion processes. The accuracy of the force-field calculations depends strongly on the accuracy of the force-field model, which is extremely difficult and time-consuming to derive from either data or ab-initio calculations. The use of machine learning algorithms has the potential to make the calculation of force-fields much more efficient but has not been explored significantly for corrosion processes. If shown to be viable, the technique would have wide -ranging impact on design of corrosion resistant materials and on the mitigation of corrosion in existing process systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Machine learning for analysis of atomic spectral data

Physics based forward models are the basis on which many experimental diagnostics are interpreted. For some diagnostics, models can be computationally expensive which precludes their use in real time analysis. Reduced models have the potential to capture sufficient physics thereby enabling the desired real time analysis. Using statistical inference and machine learning techniques the application of reduced models for inversion of atomic spectral data used to diagnose magnetic fields in a plasma will be examined. Two approaches are considered, (a) a reduction of the forward model where traditional inversion can be performed on the proxy model, and (b) a reduction of the direct inverse where parameters are a function of measured signal. The resulting inversion is sufficiently fast to be utilized in an online context for digital twinning, and ultimately real-time prediction, design, and control of plasma systems, such as tokamaks. Furthermore, these methods will be demonstrated on both simulated and experimentally measured data.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2017

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.

58 GEOSCIENCES↗

DOE OSTI2026

Accelerating laser ray tracing in high fidelity physics simulations of laser melting using squeeze U-net

Laser melting is a core component of the ongoing industrial revolution, dubbed Industry 4.0, as lasers facilitate fast and precise melting and fusion in advanced manufacturing. There is a strong need to optimize the laser process using simulations. However, this has proven challenging as high fidelity simulations are needed for predictive modeling and this is currently prohibitively expensive even when run on hundreds of processors on high performance computers. The challenge is capturing complex physics of laser material interaction, fluid dynamics, thermal physics and material phase transformations at various length and time scales. To close this technological gap, we modified a squeeze U-net to accelerate the laser ray tracing component of such high fidelity models by ~4x–40x while preserving the core physics principle of conservation of energy with 97% accuracy. This approach enables the accurate modeling of global laser energy absorption as a function of local surface temperatures and complex surface topologies, which govern the reflection directions and energy losses of laser rays upon interacting with the material surface.

Computer science↗

DOE OSTI2013

Efficient calculation of many-body induced electrostatics in molecular systems

Potential energy functions including many-body polarization are in widespread use in simulations of aqueous and biological systems, metal-organics, molecular clusters, and other systems where electronically induced redistribution of charge among local atomic sites is of importance. The polarization interactions, treated here via the methods of Thole and Applequist, while long-ranged, can be computed for moderate-sized periodic systems with extremely high accuracy by extending Ewald summation to the induced fields as demonstrated by Nymand, Sala, and others. These full Ewald polarization calculations, however, are expensive and often limited to very small systems, particularly in Monte Carlo simulations, which may require energy evaluation over several hundred-thousand configurations. For such situations, it shall be shown that sufficiently accurate computation of the polarization energy can be produced in a fraction of the central processing unit (CPU) time by neglecting the long-range extension to the induced fields while applying the long-range treatments of Ewald or Wolf to the static fields; these methods, denoted Ewald E-Static and Wolf E-Static (WES), respectively, provide an effective means to obtain polarization energies for intermediate and large systems including those with several thousand polarizable sites in a fraction of the CPU time. Furthermore, we shall demonstrate a means to optimize the damping for WES calculations via extrapolation from smaller trial systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Adjoint-enabled multidimensional optimization of satellite electron/proton shields - 25452

The design of satellites usually includes the objective of minimizing mass due to high launch costs, which is complicated by the need to protect sensitive electronics from the space radiation environment. There is growing interest in automated design optimization techniques to help achieve that objective. Traditional optimization approaches that rely exclusively on response functions (e.g. dose calculations) can be quite expensive when applied to transport problems. Previously we showed how adjoint-based transport sensitivities used in conjunction with gradient-based optimization algorithms can be quite effective in designing mass-efficient electron/proton shields in one-dimensional slab geometries. In this paper we extend that work to two-dimensional Cartesian geometries. This consists primarily of deriving the sensitivities to geometric changes, given a particular prescription for parameterizing the shield geometry. We incorporate these sensitivities into our optimization process and demonstrate their effectiveness in such design calculations. (authors)

42 ENGINEERING↗

DOE OSTI2024

Force-Free Identification of Minimum-Energy Pathways and Transition States for Stochastic Electronic Structure Theories

Here, the accurate mapping of potential energy surfaces (PESs) is crucial to our understanding of the numerous physical and chemical processes mediated by atomic rearrangements, such as conformational changes and chemical reactions, and the thermodynamic and kinetic feasibility of these processes. Stochastic electronic structure theories, e.g., Quantum Monte Carlo (QMC) methods, enable highly accurate total energy calculations that in principle can be used to construct the PES. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method, previously developed for QMC structural optimization, to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and at saddle points, we show that it is possible to identify MEPs and TSs by using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia (NH 3 ) molecule and the nucleophilic substitution (S N 2) reaction F – + CH 3 F → FCH 3 + F – . We validate our results using Density Functional Theory (DFT)- and Coupled Cluster (CCSD, CCSD(T))-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities, free energy differences, rate constants, and equilibrium constants that incorporates stochastically optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations at significantly reduced computational expense.

Iyer, Gopal R.↗

DOE OSTI2025

Functional Expansion Tallies Using MCNP6 PTRAC Files [Slides]

Accuracy is vital for many radiation transport applications. Monte Carlo (MC) codes are the most accurate, but they are expensive for fine meshes. Functional Expansion Tallies (FETs) yield smoother, lower-variance results with far fewer degrees of freedom.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Monte Carlo explicitly correlated second-order many-body perturbation theory

A stochastic algorithm is proposed and implemented that computes a basis-set-incompleteness (F12) correction to an ab initio second-order many-body perturbation energy as a short sum of 6- to 15-dimensional integrals of Gaussian-type orbitals, an explicit function of the electron-electron distance (geminal), and its associated excitation amplitudes held fixed at the values suggested by Ten-no. The integrals are directly evaluated (without a resolution-of-the-identity approximation or an auxiliary basis set) by the Metropolis Monte Carlo method. Applications of this method to 17 molecular correlation energies and 12 gas-phase reaction energies reveal that both the nonvariational and variational formulas for the correction give reliable correlation energies (98% or higher) and reaction energies (within 2 kJ mol -1 with a smaller statistical uncertainty) near the complete-basis-set limits by using just the aug-cc-pVDZ basis set. The nonvariational formula is found to be 2–10 times less expensive to evaluate than the variational one, though the latter yields energies that are bounded from below and is, therefore, slightly but systematically more accurate for energy differences. Being capable of using virtually any geminal form, the method confirms the best overall performance of the Slater-type geminal among 6 forms satisfying the same cusp conditions. Lastly, not having to precompute lower-dimensional integrals analytically, to store them on disk, or to transform them in a nonscalable dense-matrix-multiplication algorithm, the method scales favorably with both system size and computer size; the cost increases only as O(n 4 ) with the number of orbitals (n), and its parallel efficiency reaches 99.9% of the ideal case on going from 16 to 4096 computer processors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

SymPix: A spherical grid for efficient sampling of rotationally invariant operators

We present SymPix, a special-purpose spherical grid optimized for efficiently sampling rotationally invariant linear operators. This grid is conceptually similar to the Gauss–Legendre (GL) grid, aligning sample points with iso-latitude rings located on Legendre polynomial zeros. Unlike the GL grid, however, the number of grid points per ring varies as a function of latitude, avoiding expensive oversampling near the poles and ensuring nearly equal sky area per grid point. The ratio between the number of grid points in two neighboring rings is required to be a low-order rational number (3, 2, 1, 4/3, 5/4, or 6/5) to maintain a high degree of symmetries. Our main motivation for this grid is to solve linear systems using multi-grid methods, and to construct efficient preconditioners through pixel-space sampling of the linear operator in question. As a benchmark and representative example, we compute a preconditioner for a linear system that involves the operator D-hat + B-hat {sup T}N{sup −1} B-hat , where B-hat and D-hat may be described as both local and rotationally invariant operators, and N is diagonal in the pixel domain. For a bandwidth limit of ℓ{sub max} = 3000, we find that our new SymPix implementation yields average speed-ups of 360 and 23 for B-hat {sup T}N{sup −1} B-hat and D-hat , respectively, compared with the previous state-of-the-art implementation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Optimal Orbital Selection for Full Configuration Interaction (OptOrbFCI): Pursuing the Basis Set Limit under a Budget

Full configuration interaction (FCI) solvers are limited to small basis sets due to their expensive computational costs. An optimal orbital selection for FCI (OptOrbFCI) is proposed to boost the power of existing FCI solvers to pursue the basis set limit under a computational budget. The optimization problem coincides with that of the complete active space SCF method (CASSCF), while OptOrbFCI is algorithmically quite different. OptOrbFCI effectively finds an optimal rotation matrix via solving a constrained optimization problem directly to compress the orbitals of large basis sets to one with a manageable size, conducts FCI calculations only on rotated orbital sets, and produces a variational ground-state energy and its wave function. Coupled with coordinate descent full configuration interaction (CDFCI), we demonstrate the efficiency and accuracy of the method on the carbon dimer and nitrogen dimer under basis sets up to cc-pV5Z. We also benchmark the binding curve of the nitrogen dimer under the cc-pVQZ basis set with 28 selected orbitals, which provide consistently lower ground-state energies than the FCI results under the cc-pVDZ basis set. Furthermore, the dissociation energy in this case is found to be of higher accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Screening and Discovery of Metal Compound Active Sites for Strong and Selective Adsorption of N 2 in Air

Photocatalytic nitrogen fixation has the potential to provide a greener route for producing nitrogen-based fertilizers under ambient conditions. Computational screening is a promising route to discover new materials for the nitrogen fixation process, but requires identifying “descriptors” that can be efficiently computed. In this work, we argue that selectivity toward the adsorption of molecular nitrogen and oxygen can act as a key descriptor. A catalyst that can selectively adsorb nitrogen and resist poisoning of oxygen and other molecules present in air has the potential to facilitate the nitrogen fixation process under ambient conditions. Here we provide a framework for active site screening based on multifidelity density functional theory (DFT) calculations for a range of metal oxides, oxyborides, and oxyphosphides. The screening methodology consists of initial low-fidelity fixed geometry calculations and a second screening in which more expensive geometry optimizations were performed. The approach identifies promising active sites on several TiO 2 polymorph surfaces and a VBO 4 surface, and the full nitrogen reduction pathway is studied with the BEEF-vdW and HSE06 functionals on two active sites. The findings suggest that metastable TiO 2 polymorphs may play a role in photocatalytic nitrogen fixation, and that VBO 4 may be an interesting material for further studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗