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DOE OSTI2022

Accelerators for Classical Molecular Dynamics Simulations of Biomolecules

Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

GPU Acceleration of Large-Scale Full-Frequency GW Calculations

Many-body perturbation theory is a powerful method to simulate electronic excitations in molecules and materials starting from the output of density functional theory calculations. By implementing the theory efficiently so as to run at scale on the latest leadership high-performance computing systems it is possible to extend the scope of GW calculations. Here, we present a GPU acceleration study of the full-frequency GW method as implemented in the WEST code. Excellent performance is achieved through the use of (i) optimized GPU libraries, e.g., cuFFT and cuBLAS, (ii) a hierarchical parallelization strategy that minimizes CPU-CPU, CPU-GPU, and GPU-GPU data transfer operations, (iii) nonblocking MPI communications that overlap with GPU computations, and (iv) mixed precision in selected portions of the code. A series of performance benchmarks has been carried out on leadership high-performance computing systems, showing a substantial speedup of the GPU-accelerated version of WEST with respect to its CPU version. Good strong and weak scaling is demonstrated using up to 25 920 GPUs. Finally, we showcase the capability of the GPU version of WEST for large-scale, full-frequency GW calculations of realistic systems, e.g., a nanostructure, an interface, and a defect, comprising up to 10 368 valence electrons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

GronOR: Scalable and Accelerated Nonorthogonal Configuration Interaction for Molecular Fragment Wave Functions

GronOR is a program package for nonorthogonal configuration interaction calculations. Electronic wave functions are constructed in terms of antisymmetrized products of multiconfiguration molecular fragment wave functions. The computational complexity of the nonorthogonal methodologies implemented in GronOR applied to large molecular assemblies requires a design that takes full advantage of massively parallel supercomputer architectures and accelerator technologies. This work describes the implementation strategy and resulting performance characteristics. In addition to parallelization and acceleration, the software development strategy includes aspects of fault resiliency and heterogeneous computing. The program was designed for large-scale supercomputers but also runs effectively on small clusters and workstations for small molecular systems. GronOR is available as open source to the scientific community.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Simulations of the Biodegradation of Citrate Based Polymers for Artificial Scaffolds Using Accelerated Reactive Molecular Dynamics

Here, we explore the reactivity and mechanical properties of poly(1,6-hexanediol-co-Citric acid) via ReaxFF molecular dynamics simulations. We implement an accelerated scheme within the ReaxFF framework to study the hydrolysis reaction of the polymer which is provided with sufficient amount of energy known as the restrain energy after a suitable pre-transition state configuration is obtained to overcome the activation energy barrier and the desired product is obtained. The validity of the ReaxFF force field is established by comparing the ReaxFF energy barriers of ester and ether hydrolysis with benchmark DFT values in literature. We perform chemical and mechanical degradation of polymer chain bundles at 300 K. We find that ester hydrolyzes faster than ether due to lower activation energy barrier of the reaction. The selectivity of the bond-boost scheme has been demonstrated by lowering the boost parameters of the accelerated simulation which almost stops the ether hydrolysis. Mechanical degradation of pre-hydrolyzed and intermittent hydrolyzed polymer bundles is performed along the longitudinal direction at two different strain rates. We find that the tensile modulus of the polymers are increases with increase in strain rates which shows that polymers show a strain dependent behavior. The tensile modulus of the Polyester-ether is higher than Polyester yet reaches yield-stress faster than Polyester. This makes Polyester more ductile than Polyester-ether.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Electrically Accelerated Mechanochemical Film Formation by a Phosphonium Phosphate Ionic Liquid: An In Situ Chemical Kinetics Investigation

Powertrains in electric vehicles are exposed to stray currents that accelerate wear and cause failure of mechanical components. These durability issues are further aggravated when using low-viscosity lubricants, which are desired for energy efficiency but create harsher contact conditions at sliding interfaces. This study investigates a phosphonium phosphate ionic liquid as a performance-enhancing additive in a low-viscosity base oil for lubricating electrified sliding interfaces. Ionic liquids can adsorb and react on contact interfaces via stress-assisted chemical reactions, generating nanometric tribofilms that provide protection against wear. However, the effect of electric fields on the mechanochemistry of ionic liquids is poorly understood, hindering their adoption in lubricants for electrified powertrains. This article reports an in situ optical interferometry study of ionic liquid derived tribofilm growth kinetics at stressed sliding/rolling interfaces under direct currents. The application of electric currents accelerated tribofilm formation up to a critical current density (∼1.4 A/mm 2 ), beyond which pitting-induced wear dominated. The tribofilms were composed of iron phosphates, iron oxides, and carbon species, with iron oxides becoming predominant under applied currents. These tribofilms prevented the scuffing failure of steel surfaces under electrified conditions. Based on the results, a kinetic model is proposed that integrates electric current effect into the classical stress-assisted thermal activation framework to allow prediction of tribofilm growth at electrified sliding contacts. This framework provides crucial guidance for designing next-generation lubricants for electrified transportation and power generation systems.

additives↗

DOE OSTI2025

Unveiling the lithium-ion transport mechanism in Li{sub 2}ZrCl{sub 6} Solid-State Electrolyte {ital via} deep learning-accelerated molecular dynamics simulations.

Lithium zirconium chlorides (LZCs) present a promising class of cost-effective solid electrolytes for next-generation all-solid-state batteries. The unique crystal structure of LZCs plays a crucial role in facilitating lithium-ion mobility, which further affects the electrochemical performance. To understand the underlying mechanism governing ion transport, we employed deep learning-accelerated molecular dynamics simulation on Li2ZrCl6 (trigonal alpha- and monoclinic beta-LZC), focusing specifically on the zirconium coordination environment. Our results reveal that disordered alpha-LZC exhibits the highest ionic conductivity, while beta-LZC demonstrates significantly lower conductivity, closely aligning with experimental findings. The study confirms that across all phases, lithium migration proceeds via the site-to-site hopping mechanism, where variations in site residence times critically impact the overall ionic conductivity. In alpha-LZCs, lithium ions prefer to anisotropically diffuse across interlayers as the result of a lower energy barrier, driven primarily by collective diffusion. In contrast, lithium ions in beta-LZC primarily isotropically diffuse within the intralayer, hindered by higher energy barriers and determined by individual diffusion. The variation in ZrCl6 2- octahedral unit softening, induced by the specific layered arrangement of zirconium atoms, emerges as a critical determinant of the energy barriers across the LZC phases. These atomic-scale insights into the transport processes provide valuable guidance for the rational design and optimization of LZCs-based electrolytes, accelerating their practical application in advanced energy storage technologies.

Guo, Hanzeng↗

DOE OSTI2020

Rational Design of a Ni 3 N 0.85 Electrocatalyst to Accelerate Polysulfide Conversion in Lithium–Sulfur Batteries

Slow kinetics of polysulfide conversion reactions lead to severe issues for lithium–sulfur (Li–S) batteries, for example, low rate capability, polysulfide migration, and low Coulombic efficiencies. These challenges hinder the practical applications of Li–S batteries. Here, we proposed a rational strategy of tuning the d-band of catalysts to accelerate the conversion of polysulfides. Nitrogen vacancies were engineered in hexagonal Ni 3 N (space group P 6 3 22) to tune its d-band center, leading to the strong interaction between polysulfides and Ni 3 N. Because of the greater electron population in the lowest occupied molecular orbital of Li 2 S 4 , the terminal S–S bonds were weakened for breaking. Temperature-dependent experiments confirm that Ni 3 N 0.85 demonstrates a much low activation energy, thereby accelerating the conversion of polysulfides. A Li–S cell using Ni 3 N 0.85 can deliver a high initial discharge capacity of 1445.9 mAh g –1 (at 0.02 C) and low decay per cycle (0.039%). The Ni 3 N 0.85 cell can also demonstrate an initial capacity of 1200.4 mAh g –1 for up to 100 cycles at a high loading of 5.2 mg cm –2 . The high efficiency of rationally designed Ni 3 N 0.85 demonstrates the effectiveness of the d-band tuning strategy to develop low-activation-energy catalysts and to promote the atomic understanding of polysulfide conversion in Li–S batteries.

25 ENERGY STORAGE↗

DOE OSTI2023

Accelerated Synthesis of Ordered Mesoporous Carbons Using Plasma

Conventional ordered mesoporous carbon (OMC) production usually requires long processing times in the carbonization step to achieve desired temperatures through controlled ramps. To enable expedited materials discovery, developing advanced manufacturing capability with significantly improved throughput is highly desired. Current approaches for accelerating the synthesis of OMCs include using microwave and Joule heating. However, both methods rely on the introduction of additional components, such as microwave absorbers and electrically conductive agents, within the bulk materials to impart the ability to reach high carbonization temperatures. This work demonstrates accelerated synthesis and functionalization of OMCs through the use of a dielectric barrier discharge plasma, where carbonization can be accomplished within 15 min using 30 W plasma sources, representing more than an order of magnitude increase in polymer-to-carbon conversion kinetics compared to that of a traditionally pyrolyzed analogue. Particularly, the ability of performing rapid carbonization without the use of additional substrates within the OMC precursor systems is advantageous. A systematic investigation of how plasma power, time, and gas atmosphere impact the resulting OMC pore textures and properties is performed, demonstrating the broad applicability of plasma-enabled carbonization methods. Furthermore, we demonstrate that the plasma treatment strategy can be extended to incorporate heteroatoms into the carbon framework by introducing ammonia gas, resulting in OMCs with a nitrogen content up to 4.7 at %, as well as non-Pluronic templating systems for synthesizing OMC with pore sizes larger than 10 nm. As employing a plasma source for materials pyrolysis is an industrially relevant approach, our system can be extended toward scaled synthesis of OMCs with much faster production rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Exploring Approaches for Accelerated Mass Spectrometric Detection of Fluorochemicals from Aqueous Solutions Using Indirect, Adsorbent-Facilitated Desorption Ionization

There is a growing need for rapid, low-cost analytical tools to screen water supplies for persistent contaminants. Due to their ubiquitous presence in the environment and their detrimental effects on human health, there is a public interest in widespread testing for perfluoroalkyl substances (PFAS). However, the most common methods for PFAS analysis can be time-intensive, requiring a preconcentration step followed by liquid chromatography–mass spectrometry (LC–MS), and are cost-prohibitive for most members of the public. To address these challenges, this study explores a novel method for rapid analysis of aqueous analytes by concentrating analytes on solid adsorbents and detecting them using mass spectrometric analysis via desorption ionization directly from the adsorbent surface. We demonstrate this general approach of indirect solution analysis (ISA) with desorption electrospray ionization (ISA-DESI) and laser desorption ionization (ISA-LDI). Various adsorbent materials were screened, including commercial mesoporous carbons, alumina, and metal–organic frameworks (MOFs). Using ISA-DESI, we consistently detect concentrations as low as 5–100 parts per trillion (ppt) for perfluorooctanoic acid (PFOA) and 10–100 ppt for perfluoroundecanoic acid (PFUnDA). This approach enables rapid detection in under 5 min, making it suitable for applications requiring trace detection with fast response times. We further demonstrate the use of ISA-DESI on a range of environmentally relevant contaminants. Advantages of this indirect solution analysis approach include simplified sample preparation, accelerated analysis, and low cost with readily available materials, making it a promising tool to accelerate mass spectrometric PFAS detection for identifying contaminated sites, performing laboratory studies, and monitoring industrial waste streams.

Bays, Nathan Robert [Sandia National Laboratories ↗

DOE OSTI2022

Water-Accelerated Photooxidation of CH 3 NH 3 PbI 3 Perovskite

Optical absorbance is used to study the kinetics of methylammonium lead iodide (MAPbI 3 ) thin film degradation in response to combinations of moisture, oxygen, and illumination over a range of temperatures. 105 degradations were conducted over 41 unique environmental conditions. We discover that water acts synergistically with oxygen in a water-accelerated photo-oxidation (WPO) pathway. This pathway is the dominant pathway at 25 °C and is 10, 100, 1000, and >1000 times faster than dry photooxidation (DPO), degradation via hydrate formation, thermal degradation, and blue light degradation, respectively. We find that the rate determining step for DPO is proton abstraction from methylammonium while for WPO it is proton abstraction from water, which occurs at a faster rate and results in water acting as an accelerant for photooxidation of MAPbI 3 . A full kinetic rate equation is derived and fitted to the data to determine activation energies and rate constants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Accelerating σ-Bond Metathesis at Sn(II) Centers

Molecular main-group hydride catalysts are attractive as cheap and Earth-abundant alternatives to transition-metal analogues. In the case of the latter, specific steric and electronic tuning of the metal center through ligand choice has enabled the iterative and rational development of superior catalysts. Analogously, building a deeper understanding of electronic structure-activity relationships for molecular main-group hydrides should facilitate the development of superior main-group hydride catalysts. Herein we report a modular Sn–Ni bimetallic system in which we systematically vary the ancillary ligand on Ni, which in turn tunes the Sn center. This tuning is probed using Sn L 1 XAS as a measure of electron density at the Sn center. We demonstrate that increased electron density at Sn centers accelerates the rate of σ-bond metathesis, and we employ this understanding to develop a highly active Sn-based catalyst for the hydroboration of CO 2 using pinacolborane. Additionally, we demonstrate that engineering London dispersion interactions within the secondary coordination sphere of Sn allows for further rate acceleration. Furthermore, these results show that the electronics of main group catalysts can be controlled without the competing effects of geometry perturbations, and that this manifests in substantial reactivity differences.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of large-scale computational discovery carried through experimental validation remain scarce, especially for materials with product applicability. In this paper, we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. Focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade’s worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines in the cloud, this process took less than 80 hours. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the Na x Li 3-x YCl 6 (0.5 ≤ x ≤ 2.5) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials are currently under experimental investigation that could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. We believe this unprecedented approach of synergistically integrating AI models and cloud HPC not only accelerates materials discovery but also showcases the potency of AI-guided experimentation in unlocking transformative scientific breakthroughs with real-world applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Role of Intense Arctic Storm in Accelerating Summer Sea Ice Melt: An In Situ Observational Study

Abstract Intense storms have been more frequently observed in the Arctic during recent years, in coincidence with extreme sea ice loss events. However, it is still not fully understood how storms drive such events due to deficient observations and modeling discrepancies. Here we address this problem by analyzing in situ observations acquired during an Arctic expedition, which uniquely captured an intense storm in summer 2016. The result shows a pronounced acceleration of sea ice loss during the storm process. Diagnostic analysis indicates a net energy loss at the ice surface, not supporting the accelerated melting. Although the open water surface gained net heat energy, it was insufficient to increase the mixed‐layer temperature to the observed values. Dynamic analysis suggests that storm‐driven increase in ocean mixing and upward Ekman pumping of the Pacific‐origin warm water tremendously increased oceanic heat flux. The thermal advection by the Ekman pumping led to a warmed mixed layer by 0.05°C–0.12°C and, in consequence, an increased basal sea ice melt rate by 0.1–1.7 cm day −1 .

58 GEOSCIENCES↗

DOE OSTI2023

Accelerating the Lagrangian Particle Tracking in Hydrologic Modeling to Continental-Scale

Unprecedented climate change and anthropogenic activities have induced increasing ecohydrological problems, which have motivated the development of large-scale modeling for solutions. Water age/quality is as important as water quantity for understanding the water cycle. However, current scientific progress in tracking water parcels at large-scale with high spatiotemporal resolutions is far behind that in water balance/quantity owing to the lack of powerful tools. EcoSLIM is a particle tracking model that works with the hydrologic model ParFlow-CLM, which couples surface-subsurface hydrology with land surface processes. Here, we demonstrate a parallel framework to accelerate EcoSLIM to continental-scale on a distributed, multi-GPU platform with CUDA-Aware MPI. In tests from catchment-, to regional-, and then to continental-scale using 25-million to 1.6-billion particles, EcoSLIM shows significant speedup and excellent parallel performance. The parallel framework is portable to atmospheric and oceanic particle tracking models, where parallelization is inadequate and a standard parallel framework is absent. Parallelized EcoSLIM is a promising tool to accelerate our understanding of the terrestrial water cycle and the upscaling of subsurface hydrology to Earth system models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Aerosol Decline Accelerates the Increasing Extreme Precipitation in China

Extreme precipitation is becoming more intense and frequent. The increasing trends in extreme precipitation in China in warm season related to changes in aerosols and greenhouse gases (GHGs) are investigated using observations, reanalysis data and model simulations. A significant accelerating increase in extreme precipitation occurred around 2010, with the trend in accumulated extreme rainfall amount (R95pTOT) increasing from 2.88 mm per decade during 2000–2010 to 22.88 mm per decade during 2010–2023. The sudden acceleration of the increasing extreme precipitation is largely attributed to the reverse in aerosol trends associated with China’s clean air actions, which affects extreme precipitation through perturbing cloud microphysics and atmospheric dynamics, accounting for half of the change in R95pTOT trends. Future aerosol reduction to achieve carbon neutrality is shown to continue to intensify the extreme precipitation, which overweighs the effect induced by GHGs, highlighting the importance of aerosol changes in modulating future climate and weather extremes.

58 GEOSCIENCES↗

DOE OSTI2020

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs 0.4 La 0.6 Mn 0.25 Co 0.75 O 3 , Cs 0.3 La 0.7 NiO 3 , SrNi 0.75 Co 0.25 O 3 , and Sr 0.25 Ba 0.75 NiO 3 , are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret “hairball” layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm’s ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods

Abstract The phase-field method is a powerful and versatile computational approach for modeling the evolution of microstructures and associated properties for a wide variety of physical, chemical, and biological systems. However, existing high-fidelity phase-field models are inherently computationally expensive, requiring high-performance computing resources and sophisticated numerical integration schemes to achieve a useful degree of accuracy. In this paper, we present a computationally inexpensive, accurate, data-driven surrogate model that directly learns the microstructural evolution of targeted systems by combining phase-field and history-dependent machine-learning techniques. We integrate a statistically representative, low-dimensional description of the microstructure, obtained directly from phase-field simulations, with either a time-series multivariate adaptive regression splines autoregressive algorithm or a long short-term memory neural network. The neural-network-trained surrogate model shows the best performance and accurately predicts the nonlinear microstructure evolution of a two-phase mixture during spinodal decomposition in seconds, without the need for “on-the-fly” solutions of the phase-field equations of motion. We also show that the predictions from our machine-learned surrogate model can be fed directly as an input into a classical high-fidelity phase-field model in order to accelerate the high-fidelity phase-field simulations by leaping in time. Such machine-learned phase-field framework opens a promising path forward to use accelerated phase-field simulations for discovering, understanding, and predicting processing–microstructure–performance relationships.

36 MATERIALS SCIENCE↗