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DOE OSTI2021

Topological data analysis of task-based fMRI data from experiments on schizophrenia

We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges represent similarity of fMRI time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding into low-dimensional structures in networks in a way that complements traditional approaches based on pairwise interactions. In the present paper, we analyze networks constructed from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients using persistent homology, which allows us to explore the persistence of topological structures such as loops at different scales in the networks. We use persistence landscapes, persistence images, and Betti curves to create output summaries from our persistent-homology calculations, and we study the persistence landscapes and images using k-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their 1-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Flag Gadgets Based on Classical Codes

Fault-tolerant syndrome extraction is a key ingredient in implementing fault-tolerant quantum computation. While conventional methods use a number of extra qubits that are linear in the weight of the syndrome, several improvements have been introduced using flag gadgets. In this work, we develop a framework to design flag gadgets using classical codes. Using this framework, we show how to perform fault-tolerant syndrome extraction for any stabilizer code with arbitrary distance using exponentially fewer qubits than conventional methods when qubit measurement and reset are relatively slow compared to a round of error correction. In particular, our method requires only ( 2 t + 1 ) t ⌈ log 2 ( w ) ⌉ flag qubits to fault-tolerantly measure a weight- w stabilizer. We further take advantage of the saving provided by our construction to fault-tolerantly measure multiple stabilizers using a single gadget and show that it maintains the same exponential advantage when it is used to fault-tolerantly extract the syndromes of quantum low-density parity-check codes. Using the developed framework, we perform computer-assisted search to find several small examples where our constructions reduce the number of qubits required. These small examples may be relevant to near-term experiments on small-scale quantum computers. Published by the American Physical Society 2024

Anker, Benjamin↗

DOE OSTI2021

What's Left for a Computational Chemist To Do in the Age of Machine Learning?

Machine learning (ML) has become a central focus of the computational chemistry community. In this paper, I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low-cost methods (e.g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open-shell transition-metal chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Assessing and advancing the potential of quantum computing: A NASA case study

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes.

Rieffel, Eleanor G.↗

DOE OSTI2019

Melter Modeling over a Range of Scales to Support Vitrification at the WTP

Computational modeling is a powerful complement to laboratory experiments and testing of systems at various scales. When properly applied and validated, computational fluid dynamics (CFD) can provide information not readily obtainable by physical testing, especially in cases where the operating environment does not permit visual observation or the insertion of typical data acquisition instrumentation as in the case of waste glass vitrification. A series of CFD and heat transfer models over a range of scales are being developed to support melter operations at the Hanford Waste Treatment and Immobilization Plant. This paper provides a description of the models and the resulting information they provide that can be used to improve melter throughput, as well as aid in evaluating and resolving operating plant issues or upsets. This work is important for the WTP operation by providing a potential for model-predictive control of the melter operation. Increases in the rate of glass production and avoiding idling can significantly decrease the duration and cost of the vitrification campaign, potentially saving billions of dollars.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Melter Modeling over a Range of Scales to Support Vitrification at the WTP - 19664

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Improving the Advancement of Women in Computer and Computational Science Research with the CRA-W Career Mentoring Workshops (Final Report)

The mission of the Computing Research Association’s Committee on Widening Participation in Computing Research (CRA-WP) is to widen the participation and improve the access, opportunities, and positive experiences of individuals from groups underrepresented in computing research and education. CRA-WP programs serve this overarching goal at all career stages; in addition, CRA-WP, through the formation of the CRA Center for Evaluating the Research Pipeline (CERP), has developed a methodology for thoroughly evaluating the success of its programs by comparing a nationwide sample of students, researchers, and faculty (non-participants) to program participants. Achieving these objectives requires that an increasing number of individuals from populations underrepresented in computing start and progress to the next stage while understanding and supporting the myriad computing pathways. CRA-WP offers programs for participants from undergraduate to senior professional levels. Different career stages need different types of interventions, and the goal of all Alliance program activities can be described within the unifying framework of Social Cognitive Career Theory, which finds that interest in and choice of a particular career path will be increased by interventions that improve one or more of the following: (1) outcome expectations (understanding and valuing the rewards of a particular outcome), (2) self-efficacy (a belief that one can successfully achieve an outcome), and (3) social supports that help one persist and overcome obstacles.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Temperature and Pressure Instrumentation for LYNM PE1 Chemical Explosive Testing

Underground chemical explosive testing has been conducted at the Nevada National Security Site under the Physics Experiment 1 (PE1) to validate explosive computer modeling and, ultimately, improve the accuracy of subsurface explosive detection. This SAND Report describes the dynamic temperature and pressure measurements within the chamber induced by the chemical explosive for the first of three experiments, PE1-A. The report details the instrumentation used for the experiment, the emplacement of the hardware, and the measured results. Dynamic temperature measurements were accomplished with the use of optical spectrometers and dynamic pressure was measured with a series of high-rated pressure transducers. This report includes details of the design and results of four cavity sensor systems used to measure early-time temperature, early-time pressure, late-time temperature, and late time pressure. The outcomes of PE1-A were used to inform the design of the remaining PE1 series experiments, PE1-B and PE1-DL.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2018

Modeling Heat Transfer and Pressurization of Polymeric Methylene Diisocyanate (PMDI) Polyurethane Foam in a Sealed Container

Polymer foam encapsulants provide mechanical, electrical, and thermal isolation in engineered systems. It can be advantageous to surround objects of interest, such as electronics, with foams in a hermetically sealed container to protect the electronics from hostile environments, such as a crash that produces a fire. However, in fire environments, gas pressure from thermal decomposition of foams can cause mechanical failure of the sealed system. In this work, a detailed study of thermally decomposing polymeric methylene diisocyanate (PMDI)-polyether-polyol based polyurethane foam in a sealed container is presented. Both experimental and computational work is discussed. Three models of increasing physics fidelity are presented: No Flow, Porous Media, and Porous Media with VLE. Each model us described in detail, compared to experiment, and uncertainty quantification is performed. While the Porous Media with VLE model matches has the best agreement with experiment, it also requires the most computational resources.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Computer-based human-machine interfaces for emergency operation - 233

The Halden Reactor Project (HRP) has many years of experience in developing and evaluating principles for computer-based human-machine interfaces (HMIs). We have synthesized the lessons learned in an integrated design concept that is implemented on a boiling water reactor (BWR) simulator in the Halden Man Machine Laboratory (HAMMLAB). The design concept is intended to alert and support the operators in monitoring and controlling the plant during normal operation, emergency situations and outages. The computer-based HMIs include an alarm system, operating displays, operating procedures and large screen displays. The design concept has been tested in a simulator study in HAMMLAB. This paper describes the design and initial testing of the computer-based emergency operating procedures (EOPs) and other displays to support the operators when using the EOPs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Characterizing the Geometry and Quantifying the Impact of Nanoscopic Electrocatalyst/Semiconductor Interfaces under Solar Water Splitting Conditions

The materials that are receiving the most attention in photoelectrochemical water splitting are metallic nanoparticle electrocatalysts (np-EC) attached to the surface of a semiconductor (SC) light absorber. In these multicomponent systems, the interface between the semiconductor and electrocatalysts critically affects performance. However, the np-EC/SC interface remains poorly understood as it is complex on atomic scales, dynamic under reaction conditions, and inaccessible to direct experimental probes. This contribution sheds light on how the electrocatalyst/semiconductor interface evolves under reaction conditions by investigating the behavior of nickel electrocatalysts (as nanoparticles and films) deposited on silicon semiconductors. Rigorous electrochemical experiments, interfacial atomistic characterization, and computational modeling are combined to demonstrate critical links between the atomistic features of the interface and the overall performance. It is shown that electrolyte-induced atomistic changes to the interface lead to (1) modulation of the charge carrier fluxes and a dramatic decrease in the electron/hole recombination rates and (2) a change in the barrier height of the interface. Additionally, the critical roles of nonidealities and electrocatalyst coverage due to interfacial geometry are explored. Each of these factors must be considered to optimize the design of metal/semiconductor interfaces which are broadly applicable to photoelectrocatalysis and photovoltaic research.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Experimental and computational design tools for industrial drying processes: Challenges in process‐limit prediction

Abstract The coating and drying of inks and slurries are important steps to manufacture a plethora of products. Drying processes, particularly, comprise energy‐intensive steps that affect product cost and quality. Prior work has highlighted failures of various multicomponent diffusivity models to conserve mass in dryer modeling and challenges in predicting process limits given variability in published values of key thermodynamic parameters. Herein, we develop a computational model and benchtop drying experiments to investigate these concerns for drying polymer‐laden coatings. Model predictions of process limits in a single‐zone drying oven demonstrate that published variability in Flory–Huggins parameter yields large variations in predicted operating temperatures above which blistering occurs. This indicates that caution should be exercised when choosing approaches to obtain or predict the Flory–Huggins parameter, and that both benchtop drying experiments and a set of additional experiments, such as sorption experiments, are needed to fully characterize and optimize a given drying process.

Parrish, Chance↗

DOE OSTI2025

Modeling Protein–Protein and Protein–Ligand Interactions by the ClusPro Team in CASP16

ABSTRACT In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein–protein and protein–ligand complex structures. For protein–protein docking, we combined physics‐based sampling—using ClusPro FFT docking and molecular dynamics—with AlphaFold (AF)‐based sampling, followed by AF‐based refinement. Our method produced numerous high‐accuracy complex models, including cases where AF alone failed, underscoring the critical role of physics‐based sampling alongside deep learning‐based refinement. For protein–ligand docking, we integrated the ClusPro LigTBM template‐based approach with a machine learning‐based confidence model for rescoring. The method preserves conserved interaction fragments derived from homologous complexes, followed by local resampling using physics‐based sampling and a diffusion model. Our template‐based strategy achieved a mean lDDT‐PLI of 0.69 across 233 targets, which was highly competitive. These results demonstrate that combining physics‐based modeling with AI‐driven refinement can significantly enhance the accuracy of both protein–protein and protein–ligand structure predictions.

Ashizawa, Ryota [Department of Applied Mathematics↗

DOE OSTI2018

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Size-Dependent Association of Cobalt Deuteride Cluster Anions Co{sub 3}D{sub n}{sup −} (n = 0–4) with Dinitrogen

Dinitrogen (N{sub 2}) activation by metal hydride species is of fundamental interest and practical importance while the role of hydrogen in N{sub 2} activation is not well studied. Herein, the structures of Co{sub 3}D{sub n}{sup −} (n = 0–4) clusters and their reactions with N{sub 2} have been studied by using a combined experimental and computational approach. The mass spectrometry experiments identified that the Co{sub 3}D{sub n}{sup −} (n = 2–4) clusters could adsorb N{sub 2} while the Co{sub 3}D{sub n}{sup −} (n = 0 and 1) clusters were inert. The photoelectron imaging spectroscopy indicated that the electron detachment energies of Co{sub 3}D{sub 2–4}{sup −} are smaller than those of Co{sub 3}D{sub 0,1}{sup −}, which characterized that it is easier to transfer electrons from Co{sub 3}D{sub 2–4}{sup −} than from Co{sub 3}D{sub 0,1}{sup −} to activate N{sub 2}. The density functional theory calculations generally supported the experimental observations. Further analysis revealed that the H atoms in the Co{sub 3}H{sub n}{sup −} (n = 2–4) clusters generally result in higher energies of the Co 3d orbitals in comparison with the Co{sub 3}H{sub n}{sup −} (n = 0 and 1) systems. By forming chemical bonds with H atoms, the Co atoms of Co{sub 3}H{sub 2–4}{sup −} are less negatively charged with respect to the naked Co{sub 3}{sup −} system, which leads to higher N{sub 2} binding energies of Co{sub 3}H{sub 2–4}N{sub 2}{sup −} than that of Co{sub 3}N{sub 2}{sup −}. .

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

Insights into mixing of non-isothermal multi-polymer melts for complex plastics recycling

Catalytic recycling or upcycling of plastics is often limited not by catalyst performance, but by transport, arising from highly viscous, non-Newtonian polymer melts. In this work, we develop a reactor-scale framework that integrates rheological measurements, constitutive modeling, computational fluid dynamics (CFD), and experiments to quantify mixing, heat transfer, and dispersion in surrogate hydrocarbon melts representing mixed plastics systems. Temperature- and shear rate-dependent viscosity of low-density polyethylene (LDPE) and high-density polyethylene (HDPE) is measured to create two surrogate polymers (PLD and PHD) that capture the dominant shear-thinning flow behavior while neglecting strong elastic effects, enabling tractable simulation of non-isothermal, polymer-melt mixing using a Carreau-Arrhenius generalized Newtonian framework. Three-dimensional CFD simulations are employed to evaluate impeller performance in PLD using mixing time, cavern volume, thermal uniformity, and interfacial area for regimes in which viscoelastic effects are not dominant. We show that magnetic stir bars commonly used in lab-scale studies produce large thermal gradients (~60 °C) and poor mixing, even under idealized power delivery and polymer flow conditions. In contrast, close-clearance anchor impellers achieve near-isothermal operation, reduce mixing times by up to 5×, and provide >90% active circulation volume. We further demonstrate that, at low pseudo-Deborah number (De*), motor power requirements can be predicted directly from shear rate-dependent rheology using the Carreau-Arrhenius framework, enabling rational selection of operating conditions. Extension to surrogate immiscible multi-polymer systems based on PLD and PHD shows that interfacial area is highly sensitive to operating conditions and impeller design, with coaxial anchor-turbine configurations enhancing dispersion by up to 4 × .

Close-clearance impellers↗

DOE OSTI2021

Comprehensive framework for data-driven model form discovery of the closure laws in thermal-hydraulics codes

The two-phase two-fluid model is a basis of many thermal-hydraulics codes used in design, licensing, and safety considerations of nuclear power plants. Thermal-hydraulics codes rely on the closure laws to close the system of conservation equations and describe the interactions between phases. These laws, derived from years of experimental investigations, are semi-empirical correlations that lack generality and have a limited range of applicability. Increase of computational power, availability of new experiments, and development of high-fidelity simulations has increased the number of validation data. The discrepancies between the code predictions and the validation data are a great source of knowledge. Missing physics that are not included in the model but are important for the considered phenomena can be discovered by propagating the information from the experimental results through the model. Furthermore, physics-discovered data-driven model form (P3DM) methodology integrates available integral effect tests and separate effects tests to determine the necessary corrections to the model form of the closure laws. In contrast to existing calibration techniques, the methodology modifies the functional form of the closure laws. Based on the functional form of the correction, the missing physics that were not included in the original model can be discovered. The methodology provides the alternative to the machine learning approach, in which the model is discovered in the form of the intractable black-box relation. In this work, the methodology was applied to the CTF subchannel code to improve the prediction of the two-phase flow phenomena.

42 ENGINEERING↗

DOE OSTI2025

Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

59 BASIC BIOLOGICAL SCIENCES↗