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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3

Cosmological Hydrodynamics at Exascale: A Trillion-Particle Leap in Capability

Resolving the most fundamental questions in cosmology requires simulations that match the scale, fidelity, and physical complexity demanded by next-generation sky surveys. To achieve the realism needed for this critical scientific partnership, detailed gas dynamics must be treated self-consistently with gravity for end-to-end modeling of structure formation. Exascale computing enables simulations that span survey-scale volumes while incorporating key astrophysical processes that shape complex cosmic structures. We present results from CRK-HACC, a cosmological hydrodynamics code built for extreme scalability. Using separation-of-scale techniques, GPU-resident tree solvers, in situ analysis pipelines, and multi-tiered I/O, CRK-HACCexecuted Frontier-E: a four trillion particle full-sky simulation, over an order of magnitude larger than previous efforts. The run achieved 513.1 PFLOPs peak performance, processing 46.6 billion particles per second and writing more than 100 PB of data in just over one week of runtime. Frontier-E marks a significant advance in predictive modeling for next-generation cosmological science.

Frontiere, Nicholas [Argonne National Laboratory (

Inverter Model Validation and Calibration Using Phasor Measurement Unit Data

As the penetration of inverter-based renewable energy resources increases in the power grid, especially at the distribution and microgrid levels, the need to accurately represent them in planning studies increases as well. However, due to the lack of well-established standard procedures, and vendor reluctance towards the detailed sharing of proprietary models, automated dynamic model validation and parameter calibration tools for inverter based resources (IBRs) remain scarce. This work presents a model validation and parameter calibration platform for representing IBRs with generic phasor-domain models. Phasor measurements of power system events are used for continuous validation using the data playback method, and model parameters are re-calibrated if a significant mismatch between measurements and model response is observed. Unique features of the proposed platform include- (a) an iterative Bayesian optimization approach towards parameter calibration to address a possible mismatch between the structures of generic models implemented in simulation softwares and actual commercial inverters, (b) error metrics designed to account for a possible mismatch between the time resolution of simulation and measurements, and (c) analysis of the measurement-simulation mismatch to provide guidance to engineering personnel regarding model shortcomings. The performance of the platform has been illustrated using both simulated data and field measurements to validate/calibrate inverter models in GridLAB-D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI

How well are hazards associated with derechos reproduced in regional climate simulations?

Abstract. A 15-member ensemble of convection-permitting regional simulations of the fast-moving and destructive derecho of 29–30 June 2012 that impacted the northeastern urban corridor of the USA is presented. This event generated 1100 reports of damaging winds, generated significant wind gusts over an extensive area of up to 500 000 km2, caused several fatalities, and resulted in widespread loss of electrical power. Extreme events such as this are increasingly being used within pseudo-global-warming experiments to examine the sensitivity of historical, societally important events to global climate non-stationarity and how they may evolve as a result of changing thermodynamic and dynamic contexts. As such it is important to examine the fidelity with which such events are described in hindcast experiments. The regional simulations presented herein are performed using the Weather Research and Forecasting (WRF) model. The resulting ensemble is used to explore simulation fidelity relative to observations for wind gust magnitudes, spatial scales of convection (as is manifest in high composite reflectivity, cREF), and both rainfall and hail production as a function of model configuration (microphysics parameterization, lateral boundary conditions (LBCs), start date, use of nudging, compiler choice, damping, and number of vertical levels). We also examine the degree to which each ensemble member differs with respect to key mesoscale drivers of convective systems (e.g., convective available potential energy and vertical wind shear) and critical manifestations of deep convection, e.g., vertical velocities, cold-pool generation, and how those properties relate to the correct characterization of the associated atmospheric hazards (wind gusts and hail). Use of a double-moment, seven-class scheme with number concentrations for all species (including hail and graupel) results in the greatest fidelity of model-simulated wind gusts and convective structure to the observations of this event. All ensemble members, however, fail to capture the intensity of the event in terms of the spatial extent of convection and the production of high near-surface wind gusts. We further show very high sensitivity to the LBCs employed and specifically that simulation fidelity is higher for simulations nested within ERA-Interim compared to ERA5. Excess convective available potential energy (CAPE) in all ensemble members after the derecho passage leads to excess production of convective cells, wind gusts, cREF > 40 dBZ, and precipitation during a frontal passage on the subsequent day. This event proved very challenging to forecast in real time and to reproduce in the 15-member hindcast simulation ensemble presented here. Future work could examine if simulations with other initial and lateral boundary conditions can achieve greater fidelity.

Shepherd, Tristan (ORCID:0000000186276419)

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)

Discrete-Event Model of WIPP Operations

The Waste Isolation Pilot Plant (WIPP) is the critical component of the Department of Energy's (DOE) Transuranic Radioactive Waste (TRU) disposition infrastructure. Quantifying the operations ofWIPP with a discrete-event model demonstrates the capability to assess efficiency, identity bottlenecks, and improve future operations. Such a model has been developed with the simulation software ExtendSim. This report outlines the structure of that model, summarizes the model's successful reproduction of the annual amount of emplaced waste, and demonstrates that WIPP is successfully receiving and emplacing waste at a rate consistent with the rate at which the waste arrives. The model serves as a first step toward future production enhancements at WIPP. Those enhancements will rely on close collaboration with the Carlsbad Field Office (CBFO), accurate interpretation and incorporation of the model results, and effective planning with other DOE Environmental Management (DOE-EM) entities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W

Integrating chromosome conformation and DNA repair in a computational framework to assess cell radiosensitivity

Objective. The arrangement of chromosomes in the cell nucleus has implications for cell radiosensitivity. The development of new tools to utilize Hi-C chromosome conformation data in nanoscale radiation track structure simulations allows for in silico investigation of this phenomenon. We have developed a framework employing Hi-C-based cell nucleus models in Monte Carlo radiation simulations, in conjunction with mechanistic models of DNA repair, to predict not only the initial radiation-induced DNA damage, but also the repair outcomes resulting from this damage, allowing us to investigate the role chromosome conformation plays in the biological outcome of radiation exposure. Approach. In this study, we used this framework to generate cell nucleus models based on Hi-C data from fibroblast and lymphoblastoid cells and explore the effects of cell type-specific chromosome structure on radiation response. The models were used to simulate external beam irradiation including DNA damage and subsequent DNA repair. The kinetics of the simulated DNA repair were compared with previous results. Main results. We found that the fibroblast models resulted in a higher rate of inter-chromosome misrepair than the lymphoblastoid model, despite having similar amounts of initial DNA damage and total misrepairs for each irradiation scenario. Significance. This framework represents a step forward in radiobiological modeling and simulation allowing for more realistic investigation of radiosensitivity in different types of cells.

59 BASIC BIOLOGICAL SCIENCES

Data for The Value of Reversible Carbon Storage in a Zero-Emissions World

Atmospheric carbon dioxide removal (CDR) is required to stabilize global temperature. CDR can be achieved via ecosystem-based approaches that are cost-effective but reversible (e.g., soil and forest management) or by more durable but expensive approaches (e.g., direct air capture coupled with geologic storage). Here, we examine trade-offs between these approaches, focusing on timing, climate impacts, and cost. We simulated reversible carbon accrual for a range of CDR contract structures using a general minimalist model of ecosystem carbon cycling, and parameterized it to simulate US agricultural soil management─specifically cover cropping─as a case study. We then quantified the resulting impact on atmospheric carbon and global temperature using a climate model emulator. We find that maintaining a patchwork of reversible CDR projects by replacing lapsed projects with new projects can reduce warming by 22–195 μ°C in 2100 and that the magnitude of this cooling effect depends on how effectively the patchwork is maintained. Long-term maintenance of reversible CDR projects requires institutional stability that cannot be guaranteed over multiple decades. Consequently, effective CDR ultimately requires replacing reversible projects with durable projects. To address this problem, we modeled the cost of replacing reversible agricultural soil CDR with geologic CDR. We found that using reversible CDR as a bridge to durable CDR is potentially more cost-effective as a global cooling strategy (0.20–0.81 billion USD per μ°C avoided) than perpetual maintenance of reversible CDR (0.32–1.31 billion USD per μ°C avoided) or an immediate transition to durable CDR (1.37–2.19 billion USD per μ°C avoided). However, we emphasize that institutional commitments to maintain reversible CDR projects cannot be guaranteed. Reliance on reversible CDR as a bridge to durable CDR therefore carries an unknown amount of risk and will only function if efforts to maintain reversible CDR are robust.

Carbon

Quantum Molecular Charge-Transfer Model for Multistep Auger–Meitner Decay Cascade Dynamics

The fragmentation of molecular cations following inner-shell decay processes in molecules containing heavy elements underpins the X-ray damage effects observed in X-ray scattering measurements of biological and chemical materials, as well as in medical applications involving Auger electron-emitting radionuclides. Traditionally, these processes are modeled using simulations that describe the electronic structure at an atomic level, thereby omitting molecular bonding effects. This work addresses the gap by introducing a novel approach that couples Auger–Meitner decay to nuclear dynamics across multiple decay steps, by developing a decay spawning dynamics algorithm and applying it to potential energy surfaces characterized with ab initio molecular dynamics simulations. We showcase the approach on a model decay cascade following K-shell ionization of IBr and subsequent Kβ fluorescence decay. We examine two competing channels that undergo two decay steps, resulting in ion pairs with a total 3+ charge state. This approach provides a continuous description of the electron transfer dynamics occurring during the multistep decay cascade and molecular fragmentation, revealing the combined inner-shell decay and charge transfer time scale to be approximately 75 fs. In conclusion, our computed kinetic energies of ion fragments show good agreement with experimental data.

Ab initio molecular dynamics

Computational Modeling of Molten Salt Infiltration and Oxidation in Nuclear Graphite

Graphite is utilized as a moderator and reflector in advanced nuclear reactor designs due to its high thermal conductivity, neutron moderation properties, and resistance to radiation damage. However, its longterm performance and reliability are challenged by degradation mechanisms such as molten salt infiltration in molten salt reactors (MSRs) and oxidation in gas-cooled reactors (GCRs). These mechanisms can compromise the structural integrity and operational lifetime of graphite components, necessitating a more detailed assessment of their physical behavior. This report focuses on the development of computational models for molten salt infiltration and oxidation of graphite to aid the design and performance analysis of graphite components. For molten salt infiltration, a computational framework is developed that couples incompressible Navier-Stokes and phase-field model to simulate the penetration of molten salt into graphite?s interconnected pore structure. Initial model verification is performed using two-phase flows in two dimensions, demonstrating the models ability to capture fundamental physical behavior and agree with analytical solution. This framework is then applied to a realistic IG110 nuclear graphite , where a computed tomography extracted pore geometry is used to analyse the infiltration behavior of FLiNaK molten salt. This model provides insights into how the microstructure and other relevant parameters influence the transport pathways of molten salt into graphite, potentially offering a means to rapidly evaluate a graphite grade?s resistance to infiltration. For oxidation, the report details pore-scale mass and heat transport models, describing the diffusion of gases, reaction kinetics, and thermal effects. Additionally, this report highlights inconsistencies in the existing volume-averaged macroscopic model, particularly in upscaling of reaction kinetics and flux terms, and surface to volume transformations. These inconsistencies suggest that current formulations may not accurately capture the experimentally observed graphite oxidation process, highlighting the need for improved model development. This work advances the development of physics-based computational models for graphite degradation, contributing to improved predictive models for next-generation nuclear reactor designs. Future efforts will focus on refining the infiltration model to address non-physical behaviors and enhance its robustness. Additionally, for oxidation, further studies will employ the principles of volume averaging to rigorously derive the upscaled equations, potentially in collaboration with subject matter experts.

Computational Modeling of Molten Salt Infiltration

Comparative Pore Structure and Dynamics for Bacterial Microcompartment Shell Protein Assemblies in Sheets or Shells

Bacterial microcompartments (BMCs) are protein-bound organelles found in some bacteria that encapsulate enzymes for enhanced catalytic activity. These compartments spatially sequester enzymes within semipermeable shell proteins, analogous to many membrane-bound organelles. The shell proteins assemble into multimeric tiles; hexamers, trimers, and pentamers, and these tiles self-assemble into larger assemblies with icosahedral symmetry. While icosahedral shells are the predominant form in vivo , the tiles can also form nanoscale cylinders or sheets. The individual multimeric tiles feature central pores that are key to regulating transport across the protein shell. Our primary interest is to quantify pore shape changes in response to alternative component morphologies at the nanoscale. We used molecular modeling tools to develop atomically detailed models for both planar sheets of tiles and curved structures representative of the complete shells found in vivo . Subsequently, these models were animated using classical molecular dynamics simulations. From the resulting trajectories, we analyzed the overall structural stability, water accessibility to individual residues, water residence time, and pore geometry for the hexameric and trimeric protein tiles from the Haliangium ochraceu m model BMC shell. These exhaustive analyses suggest no substantial variation in pore structure or solvent accessibility between the flat and curved shell geometries. We additionally compare our analysis to hydroxyl radical footprinting data to serve as a check against our simulation results, highlighting specific residues where water molecules are bound for a long time. Although with little variation in morphology or water interaction, we propose that the planar and capsular morphology can be used interchangeably when studying permeability through BMC pores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Efficient derivative computation for unsteady fatigue-constrained nonlinear aero-structural wind turbine blade optimization

Gradient-based optimization offers significant efficiency advantages for wind turbine blade design, but its application has often been limited by the cost and accuracy of finite-difference derivative calculations, especially when fatigue constraints are considered. In this work, we systematically compare and evaluate four differentiation techniques, namely algorithmic differentiation, implicit differentiation, sparsity exploitation, and parallelization, to determine their effectiveness in computing accurate gradients through time-domain aero-structural simulations. By integrating these techniques with unsteady nonlinear aerodynamic and structural models, we develop software designed for accurate gradient computation. We show that combining these techniques addresses memory and runtime challenges associated with long simulations required by design load cases. Specifically, the most effective combination reduces derivative computation wall time by over an order of magnitude compared to finite differencing while maintaining superior accuracy. We demonstrate this approach in a proof-of-concept aero-structural optimization of a wind turbine blade that improves the cost of energy by 12.78 %. This comparative study establishes a viable approach for fatigue-aware blade design that balances computational efficiency with modeling accuracy.

17 WIND ENERGY

Repetitive proteins that undergo large conformational changes evade structural prediction algorithms

Protein structure prediction algorithms, such as AlphaFold, have accelerated protein design and advanced the understanding of the relationship between amino acid sequence and protein structure. However, these algorithms are limited in their ability to predict the structures of conformationally dynamic, intrinsically disordered, and stimuli-responsive proteins. To evaluate sequence-to-structure predictions of such challenging proteins, we explored a class of conformationally dynamic, repeats-in-toxin (RTX) proteins. RTX proteins adopt intrinsically disordered conformations in the absence of calcium and undergo reversible folding into β-roll structures upon binding to calcium. RTX proteins are characterized by tandem repeats of the sequence GGXGXDXUX, in which X can be any amino acid and U is an aliphatic amino acid. We designed RTX sequence variants with global substitutions of nonconserved amino acids, tandem repeats of consensus sequences GGAGXDTLY, and tandem repeats of scrambled sequences GGAGXDTYL. AlphaFold2 and AlphaFold3 predicted that all of these RTX variants adopt β-roll structures, characteristic of wild-type RTX bound to calcium. However, modeling the predicted structures with molecular dynamics simulations and characterizing the protein variants with circular dichroism spectroscopy, small-angle x-ray scattering, and x-ray crystallography revealed that variants adopt diverse, sequence-dependent structures in the absence and presence of calcium. To better design proteins for applications in biotechnology and sustainability, it is critical to build predictive tools that consider intrinsically disordered protein states and validate these tools with multi-mode, multi-scale experimental data.

Chang, Marina P. [Stanford Univ., CA (United State

Comparing ICME simulations with scaled laboratory experiment

In stellar physics and astrophysics, numerical simulations and laboratory experiments are often compared to observational data to support their representation of the real world. However, there is also merit in comparing numerical simulations to properly scaled experiments, especially when the experiment and the simulation are both emulating the solar phenomena. Confirming the credibility of scaled experiments and their scaling with well-validated models is important to expand our knowledge of the associated physical phenomena. This is significant because experiments and simulations can be performed frequently, whereas observations may be limited by location, field of view, and missing data. In this work, we use the Alfvén Wave Solar atmosphere Model, a well-validated magnetohydrodynamic model, to simulate an interplanetary coronal mass ejection (ICME) and compare it to an experiment which provides a scaled analog to a physical ICME. The experiment was performed on the Big Red Ball facility and scaled using dimensionless parameters such as plasma β and magnetosonic Mach number to reproduce the main structure of an ICME. We compare the model-simulated temperature, density, and magnetic field to those from the experiment, as well as the scaling parameters used in the experiment, to those calculated from the simulation. This comparison is performed to further justify the scaling arguments made by the experiment. Additionally, the comparison would lead to the development of stronger scaling arguments for future experiments.

Bryant, K. [University of Michigan, Ann Arbor, MI

Pattern-enhanced Resonant Soft X-ray Scattering for Operando monitoring of electrochemical solid-liquid interfaces

Unveiling interfaces at sub-nanometer scales is essential for advancing the understanding of complex chemical transformations. However, characterizing solid-liquid interfaces with high dimensional sensitivity and temporal resolution remains challenging, due to their dynamic nature and inaccessibility by conventional probes. Here we present an approach, Pattern-enhanced Resonant Soft X-ray Scattering, to overcome the challenges. Rooted in a “sample-as-optics” philosophy, this technique utilizes precisely engineered line-grating nanopatterns to modulate near-field X-ray illumination, coherently enhancing scattering signals from the line-gratings. We implement the method using Ni line-grating nanopatterns in electrochemical water oxidation. The periodic nanostructures serve as diffractive optical elements to reveal the Ni oxidation gradients and structural dynamics at the electrode-electrolyte interfaces. Finite-element simulations corroborate the observed trends by modeling variations in compositions and structures during electrocatalysis. Through integrating advanced sample design with coherent wave nature of soft X-rays, our approach opens accessible pathways to operando exploring chemical evolution and sub-nanometer dimensional variations simultaneously in electrochemical systems. This non-destructive method is efficient and element-specific, making it valuable for probing chemical and dimensional dynamics with appropriate modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0

Abstract. Fire is a fundamental part of the Earth system, with impacts on vegetation structure, biomass, and community composition, the latter mediated in part via key fire-tolerance traits, such as bark thickness. Due to anthropogenic climate change and land use pressure, fire regimes are changing across the world, and fire risk has already increased across much of the tropics. Projecting the impacts of these changes at global scales requires that we capture the selective force of fire on vegetation distribution through vegetation functional traits and size structure. We have adapted the fire behavior and effects module, SPITFIRE (SPread and InTensity of FIRE), for use with the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a size-structured vegetation demographic model. We test how climate, fire regime, and fire-tolerance plant traits interact to determine the biogeography of tropical forests and grasslands. We assign different fire-tolerance strategies based on crown, leaf, and bark characteristics, which are key observed fire-tolerance traits across woody plants. For these simulations, three types of vegetation compete for resources: a fire-vulnerable tree with thin bark, a vulnerable deep crown, and fire-intolerant foliage; a fire-tolerant tree with thick bark, a thin crown, and fire-tolerant foliage; and a fire-promoting C4 grass. We explore the model sensitivity to a critical parameter governing fuel moisture and show that drier fuels promote increased burning, an expansion of area for grass and fire-tolerant trees, and a reduction of area for fire-vulnerable trees. This conversion to lower biomass or grass areas with increased fuel drying results in increased fire-burned area and its effects, which could feed back to local climate variables. Simulated size-based fire mortality for trees less than 20 cm in diameter and those with fire-vulnerable traits is higher than that for larger and/or fire-tolerant trees, in agreement with observations. Fire-disturbed forests demonstrate reasonable productivity and capture observed patterns of aboveground biomass in areas dominated by natural vegetation for the recent historical period but have a large bias in less disturbed areas. Though the model predicts a greater extent of burned fraction than observed in areas with grass dominance, the resulting biogeography of fire-tolerant, thick-bark trees and fire-vulnerable, thin-bark trees corresponds to observations across the tropics. In areas with more than 2500 mm of precipitation, simulated fire frequency and burned area are low, with fire intensities below 150 kW m−1, consistent with observed understory fire behavior across the Amazon. Areas drier than this demonstrate fire intensities consistent with those measured in savannas and grasslands, with high values up to 4000 kW m−1. The results support a positive grass–fire feedback across the region and suggest that forests which have existed without frequent burning may be vulnerable at higher fire intensities, which is of greater concern under intensifying climate and land use pressures. The ability of FATES to capture the connection between fire disturbance and plant fire-tolerance strategies in determining biogeography provides a useful tool for assessing the vulnerability and resilience of these critical carbon storage areas under changing conditions across the tropics.

54 ENVIRONMENTAL SCIENCES

Demand response event simulator and risk-aware bidding tool for industrial customers

Incentive Based Demand Response (IBDR) program participation delivers financial benefits to the consumers and resiliency benefits to the electricity grid. Effectively participating in these programs as an industrial consumer requires bidding strategies that balance financial risk with operational constraints. Existing bidding tools tend not to fully incorporate stochastic IBDR event modeling, program specific baseline and payment/penalty calculations, or demand reduction process control schemes that account for the cascading impacts of shutdown in complex facilities. Here, this work presents an IBDR event simulator and risk-aware bidding framework tool integrating three key components: a flexible, parameterized demand response event generator that rigorously accounts for program structures and stochasticity, a demand response operational simulation model that generates explicit control strategies for load reduction, and a Monte Carlo simulator to evaluate financial risk for varied capacity bids. A case study at a wastewater treatment plant participating in PG&E's Capacity Bidding Program demonstrates the framework's utility. In the peak capacity price month of August, optimal bidding by the wastewater treatment plant nets a mean IBDR benefit of $101,000 (67% of the August electricity bill) with 0.4% probability of a financial loss. This framework enables industrial operators to make informed bidding decisions, negotiate better program terms with demand response load aggregators, and analyze energy flexibility investments at their facilities. Ultimately, this work reduces participation barriers in IBDR programs and supports the broader goal of enhancing grid reliability and renewable energy integration.

29 ENERGY PLANNING, POLICY, AND ECONOMY

HPC Network Simulation Tuning via Automatic Extraction of Hardware Parameters

Popular HPC network interconnection simulators such as SST/macro provide a variety of configurable parameters to explore the design space of hardware components such as network interface cards (NIC), switches, and links among them. While such knobs provide flexibility to explore design trade-offs for novel hardware, manually configuring simulations for matching configurations of the existing hardware to focus on topology exploration can be cumbersome and error-prone, leading to widely inaccurate simulations. This challenge is compounded when specifications of various (proprietary) technologies are not readily available or intentionally omitted. In this work, we propose a framework to autotune the multiple network models’ simulation configurations within SST/macro using Tree-structured Parzen Estimator-based Bayesian optimization to observe the effect on simulation accuracy across different message regimes. These regimes consist of small to large message sizes and latency to bandwidth-bound messages. We provide a detailed analysis of the simulation error for four representative HPC systems. Our Bayesian optimization based autotuning framework for network models achieves a maximum of 5x improvement in accuracy over best-effort manual configurations based on available hardware specifications.

Simulation, autotuning

GPR_calculator: An on-the-fly surrogate model to accelerate massive nudged elastic band calculations

We present GPR_calculator, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of GPR_calculator, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at https://github.com/MaterSim/GPR_calculator.

Gaussian process regression