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DOE OSTI2021

Electronic Structure Theory Calculations Using Modern Architectures: KNL vs Haswell

The time to solution and parallel efficiency of several commonly used electronic structure methods (Hartree–Fock, density functional theory, second order perturbation theory, resolution of the identity second order perturbation theory, coupled cluster) are evaluated on both the Intel Xeon Haswell and the Intel Xeon Phi Knights Landing (KNL) architectures. The Haswell completes the benchmark calculations with a faster time to solution than the KNL for all molecules and methods tested. While the Haswell exhibits an average speedup of at least 3.5 relative to the KNL for all nonthreaded computations, the KNL has a better parallel efficiency than the Haswell with increasing core counts. The architectures are further tested using a more computationally costly coupled cluster method on a transition state reaction. The Haswell appears to be the best choice to minimize the time to solution, though for very large systems and high levels of theory that require memory intensive processes the superior memory hierarchy and larger on node memory of the KNL can make it a better choice. These results are used to showcase aspects of novel architectures that will increase efficiency for quantum chemistry applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

In Vitro Assembly of Diverse Bacterial Microcompartment Shell Architectures

Bacterial Microcompartments (BMCs) are organelles composed of a selectively permeable protein shell that encapsulates enzymes involved in CO2 fixation (carboxysomes) or carbon catabolism (metabolosomes). Confinement of sequential reactions by the BMC shell presumably increases the efficiency of the pathway by reducing the crosstalk of metabolites, release of toxic intermediates, and accumulation of inhibitory products. Because BMCs are composed entirely of protein and self-assemble, they are an emerging platform for engineering nanoreactors and molecular scaffolds. However, testing designs for assembly and function through in vivo expression is labor-intensive and has limited the potential of BMCs in bioengineering. Here, we developed a new method for in vitro assembly of defined nanoscale BMC architectures: shells and nanotubes. By inserting a “protecting group”, a short ubiquitin-like modifier (SUMO) domain, self-assembly ofshell proteins in vivo was thwarted, enabling preparation of concentrates of shell building blocks. Addition of the cognate protease removes the SUMO domain and subsequent mixing of the constituent shell proteins in vitro results in the self-assembly of three types of supramolecular architectures: a metabolosome shell, a carboxysome shell, and a BMC protein-based nanotube. We next applied our method to generate a metabolosome shell engineered with a hyper-basic luminal surface, allowing for the encapsulation of biotic or abiotic cargos functionalized with an acidic accessory group. This is the first demonstration of using charge complementarity to encapsulate diverse cargos in BMC shells. Finally, our work provides a generally applicable method for in vitro assembly of natural and engineered BMC-based architectures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Understanding the Influence of Chain Architecture on the Transport Quantities of Polymer Electrolytes with Covalently Bonded Anions

Here, we use a combination of experiments and coarse-grained molecular dynamics simulations to elucidate the structure–property relationships in polymer electrolytes obtained by the copolymerization of poly(vinyl ethylene carbonate─lithium styrene bis(trifluoromethanesulfonyl)imide) or p(VEC-LiSTFSI). Experiments show that the conductivity reduces with increasing anion (i.e., STFSI) fraction on the chain, and the cation transference number (t + ) is found to be dependent on the anion fraction. Furthermore, a significant fraction of unpolymerized VEC monomers are observed. Since it is inherently difficult to experimentally control the chain architecture and the amount of unpolymerized VEC in these systems, we perform coarse-grained molecular dynamics simulations on model polymer systems with different chain architectures to mimic the plausible experimental systems. Specifically, we look at the differences in transference numbers arising from (i) a random copolymer of VEC and STFSI monomers; (ii) a blend of VEC-STFSI copolymer with VEC monomers; and (iii) a ternary blend of the VEC homopolymer, STFSI homopolymers, and VEC monomers. The ternary blend model demonstrates the closest resemblance with the experimental transference numbers and diffusivities. The lithium diffusivity obtained from the coarse-grained models with VEC monomers (plasticizers) is about 1.5 times that of the model without VEC monomers, showing that the plasticizing effect of VEC monomers is modest. We rationalize the experimental observations based on aggregate and cluster analyses obtained from molecular simulations. This work reveals that polymer electrolyte chain architecture and plasticizers can critically influence the transport properties, and these parameters should be considered when designing single ion conducting polymeric electrolytes.

cluster distribution↗

DOE OSTI2017

Rational Design of High-Loading Sulfur Cathodes with a Poached-Egg-Shaped Architecture for Long-Cycle Lithium–Sulfur Batteries

A high-loading electrode is essential for establishing high-energy-density lithium-sulfur (Li-S) batteries, but it is confronted with critical challenges. Here in this paper, we present a freestanding poached-egg-shaped architecture through a facile template supported vacuum-filtration strategy and employ it as an efficient sulfur host for Li-S batteries. This unique architecture guarantees an effective encapsulation of the “sulfur yolk” inside the fully vacuum sealed framework, effectively limiting the active material loss and polysulfide diffusion. Also, the conductive and porous framework serves as an interlinked electron pathway and electrolyte channel, greatly facilitating fast electric/ionic transport along with active material reactivation and reutilization during cycling. A high peak discharge capacity (1200 mA h g -1 ), a low capacity-fade rate (0.09% cycle-1) for 500 cycles, and excellent rate capability (C/5-1C rates) are accomplished. Moreover, with such an advantageous architecture, the sulfur loading is successfully increased to 32 mg cm -2 to achieve an areal capacity of up to 16 mA h cm -2 . This work provides guidelines for realizing optimized highloading Li-S batteries.

25 ENERGY STORAGE↗

DOE OSTI2026

Investigating the Role of Polymer Architecture in Poly(methyl methacrylate) Depolymerization

Advanced architectures in polymers have garnered traction within the last two decades due to their distinct and tunable properties compared to linear analogs. However, the effects of architecture on nascent polymethacrylate depolymerization strategies remain underexplored. Herein, we investigate the depolymerization behavior of poly(methacrylate)-based star copolymers synthesized via a core-first approach. By incorporating either labile chain-end or pendent-group triggers, we demonstrate the first direct comparison of bulk depolymerization behaviors in star versus linear methacrylate copolymers under matched conditions. While pendent-triggered systems required higher loadings of N-(methacryloxy)phthalimide methacrylate (PhthMA) to achieve similar mass loss compared to linear copolymers, chain-end-initiated depolymerization showed enhanced efficiency of monomer liberation in the star topology. Furthermore, these findings highlight the importance of considering macromolecular architecture in designing sustainable polymers and provide actionable guidelines for installing depolymerization triggers based on polymer topology.

Copolymers↗

DOE OSTI2025

Synthesis and Self-Assembly of Monodisperse Graphene Nanoribbons: Access to Submicron Architectures with Long-Range Order and Uniform Orientation

Fabricating organic semiconducting materials into large-scale well-organized architectures is critical for building high performance molecular electronics. While graphene nanoribbons (GNRs) hold enormous promise for various device applications, their assembly into a well-structured monolayer or multilayer architecture poses a substantial challenge. Here we report the preparation of length-defined monodisperse GNRs via the integrated iterative binomial synthesis (IIBS) strategy and their self-assembly into submicron-architectures with long-range order, uniform orientation as well as regular layers. Further, the use of short alkyl side chains benefits forming stable multi-layers through interlocking structures. By changing the length and backbone shapes of these monodisperse GNRs, various three-dimensional assemblies including multilayer stripes, monolayer stripes, and nanowires, can be achieved, leading to different photophysical properties and band gaps. The discovery of these intriguing self-assembly behaviors of length-defined GNRs is expected to open the door for various future applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Topology-Accelerated and Selective Cascade Depolymerization of Architecturally Complex Polyesters

Despite considerable recent advances already made in developing chemically circular polymers (CPs), the current framework predominantly focuses on CPs with linear-chain structures of different monomer types. As polymer properties are determined by not only composition but also topology, manipulating the topology of the single-monomer-based CP systems from linear-chain structures to architecturally complex polymers could potentially modulate the resulting polymer properties without changing the chemical composition, thereby advancing the concept of monomaterial product design. To that end, here, we introduce a chemically circular hyperbranched polyester (HBPE), synthesized by a mixed chain-growth and step-growth polymerization of a rationally designed bicyclic lactone with a pendent hydroxyl group (BiLOH). This HBPE exhibits full chemical recyclability despite its architectural complexity, showing quantitative selectivity for regeneration of BiLOH, via a unique cascade depolymerization mechanism. Moreover, distinct differences in materials properties and performance arising from topological variations between HBPE, hb-PBiLOH, and its linear analogue, l-PBiLOH, have been revealed where generally the branched structure led to more favorable interchain interactions, and topology-amplified optical activity has also been observed for chiral (1S, 4S, 5S)-hb-PBiLOH. More intriguingly, depolymerization of l-PBiLOH proceeds through an unexpected, initial topological transformation to the HBPE polymer, followed by the faster cascade depolymerization pathway adopted by hb-PBiLOH. Overall, these results demonstrate that CP design can go beyond typical linear polymers, and rationally redesigned, architecturally complex polymers for their unique properties may synergistically impart advantages in topology-augmented depolymerization acceleration and selectivity for exclusive monomer regeneration.

depolymerization↗

DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

The quaternary architecture of RARβ–RXRα heterodimer facilitates domain–domain signal transmission

Assessing the physical connections and allosteric communications in multi-domain nuclear receptor (NR) polypeptides has remained challenging, with few crystal structures available to show their overall structural organizations. Here we report the quaternary architecture of multi-domain retinoic acid receptor beta-retinoic X receptor alpha (RAR beta-RXR alpha) heterodimer bound to DNA, ligands and coactivator peptides, examined through crystallographic, hydrogen-deuterium exchange mass spectrometry, mutagenesis and functional studies. The RAR beta ligand-binding domain (LBD) and DNA-binding domain (DBD) are physically connected to foster allosteric signal transmission between them. Direct comparisons among all the multi-domain NRs studied crystallographically to date show significant variations within their quaternary architectures, rather than a common architecture adhering to strict rules. RXR remains flexible and adaptive by maintaining loosely organized domains, while its hetero-dimerization partners use a surface patch on their LBDs to form domain-domain interactions with DBDs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

The global spectrum of tree crown architecture

Abstract Trees can differ enormously in their crown architectural traits, such as the scaling relationships between tree height, crown width and stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of terrestrial ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from 374,888 globally distributed trees, we explore how climate, disturbance, competition, functional traits, and evolutionary history constrain the height and crown width scaling relationships of 1914 tree species. We find that variation in height–diameter scaling relationships is primarily controlled by water availability and light competition. Conversely, crown width is predominantly shaped by exposure to wind and fire, while also covarying with functional traits related to mechanical stability and photosynthesis. Additionally, we identify several plant lineages with highly distinctive stem and crown forms, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legume trees in African savannas. Our study charts the global spectrum of tree crown architecture and pinpoints the processes that shape the 3D structure of woody ecosystems.

Science & Technology - Other Topics↗

DOE OSTI2025

A fault-tolerant neutral-atom architecture for universal quantum computation

Quantum error correction (QEC) is essential for the realization of large-scale quantum computers. However, owing to the complexity of operating on the encoded ‘logical’ qubits, understanding the physical principles for building fault-tolerant quantum devices and combining them into efficient architectures is an outstanding scientific challenge. Here we use reconfigurable arrays of up to 448 neutral atoms to implement the key elements of a universal, fault-tolerant quantum processing architecture and experimentally explore their underlying working mechanisms. We first use surface codes to study how repeated QEC suppresses errors, demonstrating 2.14(13)x below-threshold performance in a four-round characterization circuit by leveraging atom loss detection and machine learning decoding. We then investigate logical entanglement using transversal gates and lattice surgery and extend it to universal logic through transversal teleportation with three-dimensional [[15,1,3]] codes, enabling arbitrary-angle synthesis with polylogarithmic overhead. Finally, we develop mid-circuit qubit reuse16, increasing experimental cycle rates by two orders of magnitude and enabling deep-circuit protocols with dozens of logical qubits and hundreds of logical teleportations with [[7,1,3]] and high-rate [[16,6,4]] codes while maintaining constant internal entropy. Our experiments show key principles for efficient architecture design, involving the interplay between quantum logic and entropy removal, judiciously using physical entanglement in logic gates and magic state generation, and leveraging teleportations for universality and physical qubit reset. These results establish foundations for scalable, universal error-corrected processing and its practical implementation in neutral atom systems.

atomic and molecular physics↗

DOE OSTI2025

Impacts of molecular architecture on the radiation-induced degradation and reaction kinetics of hydrophobic diglycolamides with the solvated electron and the dodecane radical cation

Given their proposed use as trivalent actinide–lanthanide separation ligands, the role of molecular architecture on the radiation robustness of diglycolamide (DGA) molecules has been investigated. This study examined three prototypical molecules with differences in their aliphatic chain architecture: N,N,N′,N′-tetra(n-octyl)diglycolamide (TODGA), N,N,N′,N′-tetra(2-ethylhexyl)diglycolamide (T2EHDGA), and N,N′-dimethyl-N,N′-dioctyldiglycolamide (DMDODGA). Rate coefficients and activation parameters are reported for the reactivity of each DGA with the solvated electron (e solv − ) and the corresponding dodecane radical cation (RH˙ + ) over the temperature range of 10.0 to 44.1 °C. These measurements indicate that DMDODGA is the most chemically reactive with both transient radicals, which may be attributed to this molecule's more accessible backbone. Complementary gamma dose accumulation studies (≤ 600 kGy) under envisioned process conditions—50 mM DGA in n-dodecane solvent—afforded dose constants for the loss of DGA of d = (3.41 ± 0.07) × 10 −3 , (4.19 ± 0.09) × 10 −3 , and (4.65 ± 0.09) × 10 −3 kGy −1 for T2EHDGA, DMDODGA, and TODGA, respectively. These dose constants indicate that varying DGA architecture affords subtle differences in chemical reactivity, leading to varying rates of radiolytic degradation under envisioned actinide–lanthanide separation conditions. However, more ambitious DGA frameworks, such as modifying the backbone, branching of the aliphatic chains, and/or changing the size of the chain may be required for larger gains in radiolytic longevity while optimizing actinide–lanthanide selectivity.

Arrhenius parameters↗

DOE OSTI2015

Influence of macromolecular architecture on necking in polymer extrusion film casting process

Extrusion film casting (EFC) is an important polymer processing technique that is used to produce several thousand tons of polymer films/coatings on an industrial scale. In this research, we are interested in understanding quantitatively how macromolecular chain architecture (for example long chain branching (LCB) or molecular weight distribution (MWD or PDI)) influences the necking and thickness distribution of extrusion cast films. We have used different polymer resins of linear and branched molecular architecture to produce extrusion cast films under controlled experimental conditions. The necking profiles of the films were imaged and the velocity profiles during EFC were monitored using particle tracking velocimetry (PTV) technique. Additionally, the temperature profiles were captured using an IR thermography and thickness profiles were calculated. The experimental results are compared with predictions of one-dimensional flow model of Silagy et al{sup 1} wherein the polymer resin rheology is modeled using molecular constitutive equations such as the Rolie-Poly (RP) and extended Pom Pom (XPP). We demonstrate that the 1-D flow model containing the molecular constitutive equations provides new insights into the role of macromolecular chain architecture on film necking.{sup 1}D. Silagy, Y. Demay, and J-F. Agassant, Polym. Eng. Sci., 36, 2614 (1996)

36 MATERIALS SCIENCE↗

DOE OSTI2020

Scalable molecular dynamics on CPU and GPU architectures with NAMD

NAMD is a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

high-performance computing↗

DOE OSTI2023

Densified vertically lamellar electrode architectures for compact energy storage

As one of the most compact electrochemical energy storage systems, lithium-ion batteries (LIBs) are playing an indispensable role in the process of vehicle electrification to accelerate the shift to sustainable mobility. Making battery electrodes thicker is a promising strategy for improving the energy density of LIBs which is essential for applications with weight or volume constraints, such as electric-powered transportation; however, their power densities are often significantly restricted due to elongated and tortuous charge traveling distances. Here, we propose an effective methodology that couples bidirectional freeze-casting and compression-induced densification to create densified vertically lamellar electrode architectures for compact energy storage. The vertically lamellar architectures not only overcome the critical thickness limit for conventional electrodes but also facilitate and redistribute the lithium-ion flux enabling both high rate capability and stable cyclability. Furthermore, this proposed methodology is universal as demonstrated in various electrochemical active material systems. This study offers a facile approach that realizes simultaneous high energy and high power in high-loading battery electrodes and provides useful rationales in designing electrode architectures for scalable energy storage systems.

42 ENGINEERING↗

DOE OSTI2026

Supporting multiple hardware architectures at CMS: the integration and validation of POWER9

Computing resources in the Worldwide LHC Computing Grid (WLCG) have been based entirely on the x86 architecture for more than two decades. In the near future, however, heterogeneous non-x86 resources, such as ARM, POWER and Risc-V, will become a substantial fraction of the resources that will be provided to the LHC experiments, due to their presence in existing and planned world-class HPC installations. The CMS experiment, one of the four large detectors at the LHC, has started to prepare for this situation, with the CMS software stack (CMSSW) already compiled for multiple architectures. In order to allow for a production use, the tools for workload management and job distribution need to be extended to be able to exploit heterogeneous architectures. Profiting from the opportunity to exploit the first sizable IBM Power9 allocation available on Marconi100 HPC system at CINECA, CMS developed all the needed modifications to the CMS workload management system. After a successful proof of concept, a full physics validation has been performed in order to bring the system in production. The experiences are of very high value, when it comes to commissioning of the similar (even larger) Summit HPC system at Oak Ridge, where CMS is also expecting a resource allocation. Moreover the compute power of those systems is being provided also via GPUs and this represents an extremely valuable opportunity to exploit the offloading capability already implemented in CMSSW. The status of the current integration including the exploitation of the GPUs, the results of the validation as well as the future plans will be shown and discussed.

Boccali, Tommaso [INFN, Pisa]↗

DOE OSTI2022

Vertex finding in neutrino-nucleus interaction: a model architecture comparison

We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

A lightweight, user-configurable detector ASIC digital architecture with on-chip data compression for MHz X-ray coherent diffraction imaging

Today, most X-ray pixel detectors used at light sources transmit raw pixel data off the detector ASIC. With the availability of more advanced ASIC technology nodes for scientific application, more digital functionalities from the computing domains (e.g., compression) can be integrated directly into a detector ASIC to increase data velocity. In this paper, we describe a lightweight, user-configurable detector ASIC digital architecture with on-chip compression which can be implemented in 130 nm technologies in a reasonable area on the ASIC periphery. In addition, we present a design to efficiently handle the variable data from the stream of parallel compressors. The architecture includes user-selectable lossy and lossless compression blocks. The impact of lossy compression algorithms is evaluated on simulated and experimental X-ray ptychography datasets. This architecture is a practical approach to increase pixel detector frame rates towards the continuous 1 MHz regime for not only coherent imaging techniques such as ptychography, but also for other diffraction techniques at X-ray light sources.

47 OTHER INSTRUMENTATION↗