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DOE OSTI2022

Running Ensemble Workflows at Extreme Scale: Lessons Learned and Path Forward

The ever-increasing volumes of scientific data combined with sophisticated techniques for extracting information from them have led to the increasing popularity of ensemble workflows which are a collection of runs of individual workflows. A traditional approach followed by scientists to run ensembles is to rely on simple scripts to execute different runs and manage resources. This approach is not scalable and is error-prone, thereby motivating the development of workflow management systems that specialize in executing ensembles on HPC clusters. However, when the size of both the ensemble and the target system reach extreme scales, existing workflow management systems face new challenges that hamper their efficient execution. In this paper, we describe our experience scaling an ensemble workflow from the computational biology domain from the early design stages to the execution at extreme scale on Summit, a leadership class supercomputer at the Oak Ridge National Laboratory. We discuss challenges that arise when scaling ensembles to several million runs on thousands of HPC nodes. We identify challenges with composition of the ensemble itself, its execution at large scale, post-processing of the generated data, and scalability of the file system. Based on the experience acquired, we develop a generic vision of the capabilities and abstractions to add to existing workflow management systems to enable the execution of ensemble workflows at extreme scales. We believe that the understanding of these fundamental challenges will help application teams along with workflow system developers with designing the next generation of infrastructure for composing and executing extreme-scale ensemble workflows.

Mehta, Kshitij↗

DOE OSTI2020

On the Correspondence between Seasonal Forecast Biases and Long-Term Climate Biases in Sea Surface Temperature

In this paper, the correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multi-Model Ensemble and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and sub-tropics than over the extra-tropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with times scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean-atmosphere models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Single-Point Extrapolation to the Complete Basis Set Limit through Deep Learning

Machine learning (ML) offers an attractive method for making predictions about molecular systems while circumventing the need to run expensive electronic structure calculations. Once trained on ab initio data, the promise of ML is to deliver accurate predictions of molecular properties that were previously computationally infeasible. In this work, we develop and train a graph neural network model to correct the basis set incompleteness error (BSIE) between a small and large basis set at the RHF and B3LYP levels of theory. Our results show that, when compared to fitting to the total potential, an ML model fitted to correct the BSIE is better at generalizing to systems not seen during training. We test this ability by training on single molecules while evaluating on molecular complexes. We also show that ensemble models yield better behaved potentials in situations where the training data is insufficient. However, even when only fitting to the BSIE, acceptable performance is only achieved when the training data sufficiently resemble the systems one wants to make predictions on. As a result, the test error of the final model trained to predict the difference between the cc-pVDZ and cc-pV5Z potential is 0.184 kcal/mol for the B3LYP density functional, and the ensemble model accurately reproduces the large basis set interaction energy curves on the S66x8 dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Nanoscale wetting controls reactive Pd ensembles in synthesis of dilute PdAu alloy catalysts

The performance of bimetallic dilute alloy catalysts is largely determined by the size of minority metal ensembles on the nanoparticle surface. By analyzing the synthesis of catalysts comprising Pd 8 Au 92 nanoparticles supported on silica using surface-sensitive techniques, we report that whether Pd overgrowth occurs before or after Au nanoparticle deposition onto the support controls the surface Pd ensemble size and abundance. These differences in Pd ensembles influence catalytic reactivity in H 2 –D 2 isotope exchange and benzaldehyde hydrogenation, which, in correlation with theoretical calculations, is used to elucidate the active site(s) in each reaction. To clarify how the synthetic sequence controls the formation of Pd ensembles, we combine numerical wetting calculations and molecular dynamics simulations (with a machine-learned force field) to visualize Pd deposition and migration on the nanoparticle surface, respectively. Our results suggest that the nanoparticle–support interface restricts nanoparticle accessibility to Pd deposition, which consequently controls the Pd ensemble size, illustrating the critical role of nanoscale wetting phenomena during bimetallic catalyst preparation.

36 MATERIALS SCIENCE↗

DOE OSTI2023

The endpoint of partial deconfinement

We study the matrix quantum mechanics of two free hermitian N × N matrices subject to a singlet constraint in the microcanonical ensemble. This is the simplest example of a theory that at large N has a confinement/deconfinement transition. In the microcanonical ensemble, it also exhibits partial deconfinement with a Hagedorn density of states. We argue that the entropy of these configurations, based on a combinatorial counting of Young diagrams, are dominated by Young diagrams that have the VKLS shape. When the shape gets to the maximal depth allowed for a Young diagram of SU(N), namely N, we argue that the system stops exhibiting the Hagedorn behavior. The number of boxes (energy) at the transition is N 2 /4, independent of the charge of the state.

1/N Expansion↗

DOE OSTI2021

Monte carlo simulations and experiments of all-silica zeolite LTA assembly combining structure directing agents that match cage sizes

In this work, we investigated the influence of organic structure-directing agents (OSDAs) on the formation rates of all-silica zeolite LTA using both simulations and experiments, to shed light on the crystallization process. We compared syntheses using one OSDA with a diameter close to the size of the large cavity in LTA, and two OSDAs of diameters matching the sizes of both the small and large LTA cavities. Reaction-ensemble Monte Carlo (RxMC) simulations predict a speed up of LTA formation using two OSDAs matching the LTA pore sizes; this qualitative result is confirmed by experimental studies of crystallization kinetics, which find a speedup in all-silica LTA crystallization of a factor of 3. Analyses of simulated rings and their Si–O–Si angular energies during RxMC crystallizations show that all ring sizes in the faster crystallization exhibit lower angular energies, on average, than in the slower crystallization, explaining the origin of the speedup through packing effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

An RNA aptamer exploits exosite-dependent allostery to achieve specific inhibition of coagulation factor IXa

Hemostasis relies on a reaction network of serine proteases and their cofactors to form a blood clot. Coagulation factor IXa (protease) plays an essential role in hemostasis as evident from the bleeding disease associated with its absence. RNA aptamers specifically targeting individual coagulation factors have potential as anticoagulants and as probes of the relationship between structure and function. Here, we report X-ray structures of human factor IXa without a ligand bound to the active site either in the apo-form or in complex with an inhibitory aptamer specific for factor IXa. The aptamer binds to an exosite in the catalytic domain and allosterically distorts the active site. Our studies reveal a conformational ensemble of IXa states, wherein large movements of Trp 215 near the active site drive functional transitions between the closed (aptamer-bound), latent (apo), and open (substrate-bound) states. The latent state of the apo-enzyme may bear on the uniquely poor catalytic activity of IXa compared to other coagulation proteases. The exosite, to which the aptamer binds, has been implicated in binding VIIIa and heparin, both of which regulate IXa function. Our findings reveal the importance of exosite-driven allosteric modulation of IXa function and new strategies to rebalance hemostasis for therapeutic gain.

Science & Technology - Other Topics↗

DOE OSTI2026

The properties of the nitrogen-vacancy center in milled chemical vapor deposition nanodiamonds

Fluorescent nanodiamonds (FNDs) containing negatively charged nitrogen-vacancy (NV − ) centers are vital for many emerging quantum sensing applications from magnetometry to intracellular sensing in biology. However, developing a scalable fabrication method for FNDs hosting color centers with consistent bulk-like photoluminescence (PL) and spin coherence properties remains a highly desired but unrealized goal. Here, we investigate optimized ball milling of single-crystal diamonds produced via chemical vapor deposition (CVD) and containing 2 ppm of substitutional nitrogen and 0.3 ppm of NV − to achieve this goal. The NV charge state, PL lifetime, and spin properties of bulk CVD diamond samples are directly compared to milled CVD FNDs and commercial high-pressure high-temperature (HPHT) FNDs. We find that on average, the relative contribution of the NV − charge state to the total NV PL is lower and the NV PL lifetime is longer in CVD FNDs compared to HPHT FNDs, both likely due to the lower N s 0 concentration in CVD FNDs. The CVD bulk and CVD FNDs on average show similar average T 1 spin relaxation times of 3.2 ± 0.7 ms and 4.7 ± 1.6 ms, respectively, compared to 0.17 ± 0.01 ms for commercial HPHT FNDs. Our results demonstrate that ball milling of CVD diamonds enables the large-scale fabrication of NV ensembles in FNDs with bulk-like T 1 spin relaxation properties.

chemical vapor deposition↗

DOE OSTI2020

Ab Initio Nuclear Thermodynamics

Here, we propose a new Monte Carlo method called the pinhole trace algorithm for ab initio calculations of the thermodynamics of nuclear systems. For typical simulations of interest, the computational speedup relative to conventional grand-canonical ensemble calculations can be as large as a factor of one thousand. Using a leading-order effective interaction that reproduces the properties of many atomic nuclei and neutron matter to a few percent accuracy, we determine the location of the critical point and the liquid-vapor coexistence line for symmetric nuclear matter with equal numbers of protons and neutrons. We also present the first ab initio study of the density and temperature dependence of nuclear clustering.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

From the black hole conundrum to the structure of quantum gravity

Here, we portray the structure of quantum gravity emerging from recent progress in understanding the quantum mechanics of an evaporating black hole. Quantum gravity admits two different descriptions, based on Euclidean gravitational path integral and a unitarily evolving holographic quantum system, which appear to present vastly different pictures under the existence of a black hole. Nevertheless, these two descriptions are physically equivalent. Various issues of black hole physics — including the existence of the interior, unitarity of the evolution, the puzzle of too large interior volume, and the ensemble nature seen in certain calculations — are addressed very differently in the two descriptions, still leading to the same physical conclusions. The perspective of quantum gravity developed here is expected to have broader implications beyond black hole physics, especially for the cosmology of the eternally inflating multiverse.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

58 GEOSCIENCES↗

DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Atmospheric river representation in the Energy Exascale Earth System Model (E3SM) version 1.0

Abstract. The Energy Exascale Earth System Model (E3SM) project is an ongoing, state-of-the-science Earth system modeling, simulation, and prediction project developed by the US Department of Energy (DOE). With an emphasis on supporting the DOE's energy mission, understanding and quantifying how well the model simulates water cycle processes is of particular importance. Here, we evaluate E3SM version 1.0 (v1.0) for its ability to represent atmospheric rivers (ARs), which play significant roles in water vapor transport and precipitation. The characteristics and precipitation associated with global ARs in E3SM at standard resolution (1∘ × 1∘) are compared to the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). Global patterns of AR frequencies in E3SM show high degrees of correlation (≥0.97) with MERRA2 and low mean absolute errors (MAEs; <1 %) annually, seasonally, and across different ensemble members. However, some large-scale condition biases exist, leading to AR biases – most significant of which are the double intertropical convergence zone (ITCZ), a stronger and/or equatorward-shifted subtropical jet during boreal and austral winters, and enhanced Northern Hemisphere westerlies during summer. By comparing atmosphere-only and fully coupled simulations, we attribute the sources of the biases to the atmospheric component or to a coupling response. Using relationships revealed in Dong et al. (2021), we provide evidence showing the stronger North Pacific jet in winter and the enhanced Northern Hemisphere westerlies during summer, associated with E3SM's double ITCZ and related weaker Atlantic meridional overturning circulation (AMOC), respectively, which are significant sources of the AR biases found in the coupled simulations.

58 GEOSCIENCES↗

DOE OSTI2024

Data assimilation for combustion ignition delay time simulation using schlieren image velocimetry

This study sought to improve the accuracy of simulating spray penetration and combustion ignition delay by means of data assimilation (DA). The simulations were conducted using the Reynolds-averaged Navier–Stokes (RANS) equations and assimilating the schlieren image data. In DA, an ensemble square root filter (EnSRF) was used to build the statistical model, making the simulation results more accurate without any change in the governing equations. Recognizing that the spray-cone injection angle has a large effect on penetration, we created ensemble members with different injection angles. And we applied the two-component velocity distribution calculated via SIV and updated both velocity and temperature by using a DA statistical model derived from RANS ensemble simulations. The ignition delay time is generally known to vary even under the same experimental conditions because it is influenced by many factors. In this study, we attempted the transient DA-assisted RANS simulation to predict the ignition delay time even when the temporal resolution and accuracy of the observation data ware insufficient. Our trials offer an example of how a combination of techniques can be effectively used to assimilate experimental data obtained under restricted conditions.

Combustion simulation↗

DOE OSTI2021

Tuning Gold Nanoparticle Size with Fixed Interparticle Spacing in Large-Scale Arrays: Implications for Plasmonics and Nanoparticle Etching Masks

Gold nanoparticles play an important role in plasmonic arrays, including, but not limited to, photovoltaic, biomedical, sensing, optical, and catalytic applications. For plasmonic arrays, control over spatial variables of the array, such as nanoparticle size and spacing, must be judiciously cared for. In this work, we present here a multistep deposit/dewet process for nanoparticle ensemble fabrication that exploits thermally driven solid-state diffusional dewetting, coupled with Ostwald ripening, to tune the nanoparticle size or area coverage for a particular particle–particle spacing. The initial dewetting process defines the spatial period of the array, with subsequent dewetting steps growing the nanoparticles while adhering to the “seeded” period from the first dewetting. Following the fifth deposit/dewet cycle, relative to the first cycle, the mean nanoparticle height exhibited a percent increase of 90 and 66% for 5 and 7.5 nm multistep deposit/dewet processes, respectively, while minimally impacting the ensemble period. This provides unprecedented control over the spatial parameters of large-scale nanoparticle ensembles for plasmonic and etching-mask applications where a particular particle size and spacing are desired. Due to the simplicity of the technique and its area scalability, the cyclic deposition/dewetting process is shown here to be a method that can be easily scaled up to apply to large-area (up to meter-size) applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles

Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.

Han, Mengjiao↗