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DOE OSTI2023

Genomes to Structure and Function Workshop Report 2022

The goal of the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) Program is to achieve a predictive understanding of complex biological, earth, and environmental systems with the aim of advancing the nation’s energy and infrastructure security. (https://www.energy.gov/science/ ber/biological-and-environmental-research). To pursue this goal, collaborations among experts in diverse research areas that lead to multidisciplinary projects are indispensable. The roles of DOE’s User Facilities, which offer unique and powerful resources for such research projects, are evolving, and expectations for the facilities are increasing. To respond to Users’ needs, the Joint Genome Institute (JGI) and Environmental Molecular Sciences Laboratory (EMSL) initiated the Facilities Integrating Collaborations for User Science (FICUS) program in 2014. This collaboration has grown into a popular and successful program, advancing more than 100 multidisciplinary projects to date. Similarly, the new interFacility collaborations among the JGI, EMSL, and User resources for BER structural biology and imaging at the Basic Energy Science (BES) Program’s synchrotron and neutron facilities are becoming essential for cutting-edge transdisciplinary science. To further explore the need for the BER research community to combine genomic, functional, and structural approaches to advance their research, an organizing committee was formed to develop and jointly host a 3-part workshop. The committee’s members represented seven DOE National Laboratory User Facilities (Appendix 1 lists the members). The “Genomes to Structure and Function” virtual workshop (see Appendices 2–5) was composed of three sessions. The first session, titled “Molecular Structures” (October 27– 28, 2021), highlighted diverse integrative experimental and computational approaches correlating structural data with sequencing and functional information, as well as predicting protein structures to model complex biological systems. The second session, “Intracellular Organization, and Material Synthesis and Decomposition” (December 15–16, 2021), covered imaging methods for observing, quantifying, and manipulating biosystems. The third session, “Imaging the Rhizosphere and Cellular Organization” (January 26–27, 2022) emphasized advanced and non-invasive imaging techniques applied to plant root-microbe-soil interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

3D-ReG: A 3D ReRAM-based Heterogeneous Architecture for Training Deep Neural Networks

Deep neural network (DNN) models are being expanded to a broader range of applications. The computational capability of traditional hardware platforms cannot accommodate the growth of model complexity. Among recent technologies to accelerate DNN, resistive memory (ReRAM)-based processing-in-memory (PIM) emerged as a promising solution for DNN inference due to its high efficiency for matrix-based computation. We face two major technical challenges in extending the use of ReRAM-based accelerators for training: (1) full-precision data is essential in back-propagation; (2) the need to support both feed-forward and back-propagation aggravates the data-movement burden. We propose a heterogeneous architecture named as 3D-ReG, which leverages full-precision GPU to ensure training accuracy and low-overhead 3D integration to provide low-cost data movements. Moreover, we introduce conservative and aggressive task-mapping schemes, which partition the computation phases in different ways to balance execution efficiency and training accuracy. We evaluate 3D-ReG implemented with two 3D integration technologies, through-silicon vias (TSVs) and monolithic inter-tier vias (MIVs), and compare them with GPU-only and PIM-only counterparts. Various GPU-only platforms using two main-memory technologies (DRAM, ReRAM) and three interconnect technologies (2D, TSV, MIV) are evaluated as well. Experimental results show that 3D-ReG can achieve on average 5.64× training speedup and 3.56× higher energy efficiency compared with the GPU with DRAM as main memory, at the cost of 0.05%–3.39% accuracy drop. We define a new metric, gain-loss ratio (GLR), which quantitatively evaluates the capability of a DNN training hardware in terms of the model accuracy and hardware efficiency. The results of our comparison show that the aggressive task-mapping scheme on MIV-based 3D-ReG outperforms the other methods.

Computer Science↗
DOE OSTI2022

Evaluating simplifications of subsurface process representations for field-scale permafrost hydrology models

Abstract. Permafrost degradation within a warming climate poses a significant environmental threat through both the permafrost carbon feedback and damage to human communities and infrastructure. Understanding this threat relies on better understanding and numerical representation of thermo-hydrological permafrost processes and the subsequent accurate prediction of permafrost dynamics. All models include simplified assumptions, implying a tradeoff between model complexity and prediction accuracy. The main purpose of this work is to investigate this tradeoff when applying the following commonly made assumptions: (1) assuming equal density of ice and liquid water in frozen soil, (2) neglecting the effect of cryosuction in unsaturated freezing soil, and (3) neglecting advective heat transport during soil freezing and thaw. This study designed a set of 62 numerical experiments using the Advanced Terrestrial Simulator (ATS v1.2) to evaluate the effects of these choices on permafrost hydrological outputs, including both integrated and pointwise quantities. Simulations were conducted under different climate conditions and soil properties from three different sites in both column- and hillslope-scale configurations. Results showed that amongst the three physical assumptions, soil cryosuction is the most crucial yet commonly ignored process. Neglecting cryosuction, on average, can cause 10 %–20 % error in predicting evaporation, 50 %–60 % error in discharge, 10 %–30 % error in thaw depth, and 10 %–30 % error in soil temperature at 1 m beneath the surface. The prediction error for subsurface temperature and water saturation is more obvious at hillslope scales due to the presence of lateral flux. By comparison, using equal ice–liquid density has a minor impact on most hydrological metrics of interest but significantly affects soil water saturation with an averaged 5 %–15 % error. Neglecting advective heat transport presents the least error, 5 % or even much lower, in most metrics of interest for a large-scale Arctic tundra system without apparent influence caused by localized groundwater flow, and it can decrease the simulation time at hillslope scales by 40 %–80 %. By challenging these commonly made assumptions, this work provides permafrost hydrology scientists an important context for understanding the underlying physical processes, including allowing modelers to better choose the appropriate process representation for a given modeling experiment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Notes on the complex Sachdev-Ye-Kitaev model

We describe numerous properties of the Sachdev-Ye-Kitaev model for complex fermions with N $\gg$ 1 flavors and a global U(1) charge. We provide a general definition of the charge in the ( G, Σ) formalism, and compute its universal relation to the infrared asymmetry of the Green function. The same relation is obtained by a renormalization theory. The conserved charge contributes a compact scalar field to the effective action, from which we derive the many-body density of states and extract the charge compressibility. We compute the latter via three distinct numerical methods and obtain consistent results. Finally, we present a two dimensional bulk picture with free Dirac fermions for the zero temperature entropy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Spectroscopic Evidence of Hyponitrite Radical Intermediate in NO Disproportionation at a MOF-Supported Mononuclear Copper Site

Dianionic hyponitrite (N 2 O 2 2- ) is often proposed, based on model complexes, as the key intermediate in reductive coupling of nitric oxide to nitrous oxide at the bimetallic active sites of heme-copper oxidases and nitric oxide reductases. In this work, we examine the gas-solid reaction of nitric oxide with the metal–organic framework Cu I -ZrTpmC* with a suite of in situ spectroscopies and density functional theory simulations, and identify an unusual chelating N 2 O 2 .- intermediate. These results highlight the advantage provided by site-isolation in metal–organic frameworks (MOFs) for studying important reaction intermediates, and provide a mechanistic scenario compatible with the proposed one-electron couple in these enzymes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Spectroscopic Evidence of Hyponitrite Radical Intermediate in NO Disproportionation at a MOF‐Supported Mononuclear Copper Site

Abstract Dianionic hyponitrite (N 2 O 2 2− ) is often proposed, based on model complexes, as the key intermediate in reductive coupling of nitric oxide to nitrous oxide at the bimetallic active sites of heme‐copper oxidases and nitric oxide reductases. In this work, we examine the gas‐solid reaction of nitric oxide with the metal–organic framework Cu I ‐ZrTpmC* with a suite of in situ spectroscopies and density functional theory simulations, and identify an unusual chelating N 2 O 2 .− intermediate. These results highlight the advantage provided by site‐isolation in metal–organic frameworks (MOFs) for studying important reaction intermediates, and provide a mechanistic scenario compatible with the proposed one‐electron couple in these enzymes.

Sun, Chenyue↗
DOE OSTI2021

Understanding Superatomic Ag Nanohydrides

Abstract Bulk Ag hydrides are extremely challenging to make even at very high pressures, but they may become stable as the particle size shrinks to the nanometer regime. Here, the formation and electronic structure of Ag nanohydrides are investigated from a superatomic perspective by density functional theory. It is found that as the coverage increases, adsorption energy of hydrogen atoms on Ag 38 cluster to form Ag 38 H 2 n nanohydride ( n is from 1 to 15) can be energetically favorable with respect to bare Ag 38 and H 2 . Furthermore, the adsorbed hydrogen atoms contribute their 1s electrons to the superatom electron count and behave as a metal instead of a ligand. The electronic structure of the silver nanohydrides follows the superatomic complex model, leading to magic or relatively more stable compositions such as Ag 38 H 2 , Ag 38 H 20 , and Ag 38 H 30 , which correspond to 40‐electron, 58‐electron, and 68‐electron shell closings, respectively. Angular momentum analyses of the superatomic orbitals suggest a convoluted interaction of geometry, symmetry, and orbital splitting.

He, Xiang↗
DOE OSTI2020

Hypergraph Analytics of Domain Name System Relationships

We report on the use of novel mathematical methods in hypergraph analytice over a large quantity of DNS data. Hypergraphs generalize graphs, as used in network science, to better model complex multiway relations in cyber data. Specifically, casting DNS data from Georgia Tech's ActiveDNS repository as hypergraphs allows us to fully represent the interactions between {\em collections} of domains and IP addresses. To facilitate large-scale analytics, we fielded an analytical pipeline of two capabilities. HyperNetX (HNX) is a Python package for the exploration and visualization of hypergraphs, acting as a frontend. For the backend, the Chapel HyperGraph Library (CHGL) is a library for high performance hypergraph analytics written in the exascale programming language Chapel. CHGL was used to process gigascale DNS data, performing compute-intensive calculations for data reduction and segmentation. Identified portions are then sent to HNX for both exploratory analysis and knowledge discovery targeting known tactics, techniques, and procedures.

Joslyn, Cliff A.↗
DOE OSTI2020

One Electron Multiple Proton Transfer in Model Organic Donor–Acceptor Systems: Implications for High-Frequency EPR

EPR spectroscopy is an important spectroscopic method for identification and characterization of radical species involved in many biological reactions. The tyrosyl radical is one of the most studied amino acid radical intermediates in biology. Often in conjunction with histidine residues, it is involved in many fundamental biological electron and proton transfer processes, such as in the water oxidation in photosystem II. As biological processes are typically extremely complicated and hard to control, molecular bio-mimetic model complexes are often used to clarify the mechanisms of the biological reactions. In this work, we present theoretical calculations to investigate the sensitivity of magnetic resonance parameters to proton-coupled electron transfer events, as well as conformational substates of the molecular constructs which mimic the tyrosine-histidine (Tyr-His) pairs found in a large variety of proteins. Upon oxidation of the phenol, the Tyr analog, these complexes can perform not only one-electron one-proton transfer (EPT), but also one-electron two-proton transfers (E2PT). It is shown that in aprotic environment the g X -components of the electronicg-tensor are extremely sensitive to the first proton transfer from the phenoxyl oxygen to the imidazole nitrogen (EPT product), leading to a significant increase of the g X -value of up to 0.003, but are not sensitive to the second proton transfer (E2PT). In the latter case, the change of the g X -value is much smaller (ca. 0.0001), which is too small to be distinguished even by high-frequency EPR. The 14 N hyperfine values are also too similar to allow differentiation between the different protonation states in EPT and E2PT. The magnetic resonance parameters were also calculated as a function of the rotation angles around single bonds. It was demonstrated that rotation of the phenoxyl group results in large positive changes (> 0.001) in the g X -values. Analysis of the data reveals that the main source of these changes is related to the strength of the H-bond between phenoxyl oxygen and the proton(s) on N 1 and N 2 positions of the imidazole.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Dynamics of McMillan mappings III. Symmetric map with mixed nonlinearity

This article extends the study of the dynamical properties of the symmetric McMillan map, emphasizing its utility in understanding and modeling complex nonlinear systems. Although the map features six parameters, we demonstrate that only two are irreducible: the linearized rotation number at the fixed point and a nonlinear parameter representing the ratio of terms in the biquadratic invariant. Through a detailed analysis, we classify regimes of stable motion, provide exact solutions to the mapping equations, and derive a canonical set of action-angle variables, offering analytical expressions for the rotation number and nonlinear tune shift. We further establish connections between general standard-form mappings and the symmetric McMillan map, using the area-preserving Hénon map and accelerator lattices with thin sextupole magnet as representative case studies. Our results show that, despite being a second-order approximation, the symmetric McMillan map provides a highly accurate depiction of dynamics across a wide range of system parameters, demonstrating its practical relevance in both theoretical and applied contexts.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

A new perspective on density and strength loss profiles at the surface of thermally oxidized nuclear graphite

Oxidation of graphite components could influence their designed life in a high-temperature nuclear reactor. The oxidized regions could potentially lower the allowed stress capacity. The American Society of Mechanical Engineers rules for the design and construction of graphite-moderated reactors recommend that subsurface regions that might become excessively damaged by oxidation during reactor operation be identified and excluded from geometry and stress calculations. Identification of oxidation-affected regions is possible, in principle, through complex modeling exercises of reactor behavior during hypothetical accident scenarios coupled with graphite oxidation models, but this procedure may not have the precision needed for informed decisions. Here, this paper proposes an alternate method, based on interpretation of a series of well-designed oxidation experiments, which could augment the designer's tools. The procedure is illustrated by data on oxidation by air of several graphite grades (NBG-18, PCEA, IG-110, R4-650) that are corroborated with independent literature information, when available. The Wichner model for graphite oxidation used for this analysis provides conservative results that could be quickly implemented in the design process.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Topological analysis of X-ray CT data for the recognition and trending of subtle changes in microstructure under material aging

X-ray computed tomography (CT) is an established non-destructive tool for 3D imaging of multiphasic composites. Numerous applications of X-ray CT in medical diagnosis and materials characterization have been reported, many involving field-specific innovations in the imaging technology itself. Yet, quantitative summarization to link image features to properties of interest has been rare. Here we address this issue by employing state-of-the-art technics in scalar field topology for the summarization of X-ray CT images of an example biphasic system. By varying processing-parameters we create different microstructures, evolve them through accelerated thermal aging, CT-image them pre- and post-aged, and demonstrate the ability of our image summarization method to systematically track process- and age-related changes, which can often be very subtle. A novel aspect of the algorithm involves recognition over multiple resolution levels, which provides deeper insight into the pattern relationship between grain-like features and their neighbors. The method is general, adaptable to diverse image reconstruction methods and materials systems, and particularly useful in applications where practical constraints on the sample-size limits the reliable use of more complex models, e.g., convolutional neural networks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multirate partitioned Runge–Kutta methods for coupled Navier–Stokes equations

Earth system models are complex integrated models of atmosphere, ocean, sea ice, and land surface. Coupling the components can be a significant challenge due to the difference in physics, temporal, and spatial scales. Further, this study explores multirate partitioned Runge-Kutta methods for the fluid-fluid interaction problem and demonstrates its parallel performance by using the PETSc library. We consider compressible Navier-Stokes equations with gravity coupled through a rigid-lid interface. Our large-scale numerical experiments reveal that multirate partitioned Runge-Kutta coupling schemes (1) can conserve total mass; (2) have second-order accuracy in time; and (3) provide favorable strong- and weak-scaling performance on modern computing architectures. We also show that the speedup factors of multirate partitioned Runge-Kutta methods match theoretical expectations over their base (single-rate) method.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Density functional theory↗
DOE OSTI2025

Enhancing approximate modular Bayesian inference by emulating the conditional posterior

In modular Bayesian analyses, complex models are composed of distinct modules, each representing different aspects of the data or prior information. In this context, fully Bayesian approaches can sometimes lead to undesirable feedback between modules, compromising the integrity of the inference. The “cut-distribution” prevents unwanted influence between modules by “cutting” feedback. The direct sampling (DS) algorithm is standard practice for approximating the cut-distribution, but it can be computationally intensive, especially when the number of imputations required is large. An enhanced method is proposed, the Emulating the Conditional Posterior (ECP) algorithm, which leverages emulation to increase the number of imputations. Through numerical experiment it is demonstrated that the ECP algorithm outperforms the traditional DS approach in terms of accuracy and computational efficiency, particularly when resources are constrained. Here, it is also shown how the DS algorithm can be improved using ideas from design of experiments. Some practical recommendations are given for algorithm choice in modular Bayesian analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A GPU accelerated mixed-precision Smoothed Particle Hydrodynamics framework with cell-based relative coordinates

Smoothed Particle Hydrodynamics (SPH) is essential for modeling complex large-deformation problems across various applications, requiring significant computational power. A major portion of SPH computation time is dedicated to the Nearest Neighboring Particle Search (NNPS) process. While advanced NNPS algorithms have been developed to enhance SPH efficiency, the potential efficiency gains from modern computation hardware remain underexplored. Here, this study investigates the impact of GPU parallel architecture, low-precision computing on GPUs, and GPU memory management on NNPS efficiency. Our approach employs a GPU-accelerated mixed-precision SPH framework, utilizing low precision float-point 16 (FP16) for NNPS while maintaining high precision for other components. To ensure FP16 accuracy in NNPS, we introduce a Relative Coordinated-based Link List (RCLL) algorithm, storing FP16 relative coordinates of particles within background cells. Our testing results show three significant speedup rounds for CPU-based NNPS algorithms. The first comes from parallel GPU computations, with up to a 1000x efficiency gain. The second is achieved through low-precision GPU computing, where the proposed FP16-based RCLL algorithm offers a 1.5x efficiency improvement over the FP64-based approach on GPUs. By optimizing GPU memory bandwidth utilization, the efficiency of the FP16 RCLL algorithm can be further boosted by 2.7x, as demonstrated in an example with 1 million particles. Our code is released at https://github.com/pnnl/lpNNPS4SPH.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The simulation of 3D hypervelocity spallation using a hydrocode PAGOSA with FLIP+MPM

n this work, a hydrocode PAGOSA with FLIP+MPM is presented and exercised to investigate the fracture in ductile material. The merit of PAGOSA with FLIP+MPM to solve the advection problem is first illustrated by a solid piston periodically moving in a sealed tube with air. Furthermore, the ability of PAGOSA with FLIP+MPM to capture the fracture in material is shown by a simple stretching fracture in ductile material. In both of two benchmark problems, the PAGOSA results and analytical solutions are also presented for comparison. Then PAGOSA with FLIP+MPM is used to model complex spall in ductile material, which is a challenging problem in engineering applications. The convergences of PAGOSA with FLIP+MPM—based on both the mesh size and the marker density—are investigated by monitoring free surface velocity. To further show the ability of PAGOSA with FLIP+MPM to predict fracture in ductile material, the numerical results are compared with the experimental results and other published numerical results. Moreover, the effect of the spall parameter in PAGOSA with FLIP+MPM on the numerical simulation is also analyzed by investigating the free surface velocity. Finally, the effects of the peak compressive stress, the tensile strain rate, and the loading rate on the spallation are further investigated. The numerical results show that PAGOSA with FLIP+MPM can improve PAGOSA's performance when applied to predicting fracture in ductile materials, and is robust enough to accurately predict spall fracture in ductile materials.

42 ENGINEERING↗