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At least 343 records · Page 19

ET-AL: Entropy-targeted active learning for bias mitigation in materials data

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.

36 MATERIALS SCIENCE↗

Seeking help for perinatal depression and anxiety: a systematic review of systematic reviews from an interdependent perspective

Abstract Background Seeking help for perinatal mood and anxiety disorders is crucial for women’s mental health and babies’ development, yet many women do not seek help for their condition and remain undiagnosed and untreated. This systematic review of systematic reviews aimed at summarizing and synthesizing findings from all systematic reviews on seeking help for PMAD in the context of interdependence theory, highlighting the interdependent relationship between women and healthcare providers and how it may impact women’s seeking-help process. Methods Four electronic databases were searched, and 18 studies published up to 2023 met inclusion criteria for review. Results The capability, opportunity and motivation model of behavior was used as a framework for organizing and presenting the results. Results demonstrate that seeking help for PMAD is a function of the interdependent relationship between perinatal women’s and healthcare providers’ psychological and physical capabilities, social and physical opportunities, and their reflective and automatic motivation. Conclusions Unmet needs in perinatal mental healthcare is an important public health problem. This systematic review of systematic reviews highlights key factors for policymakers, researchers, and practitioners to consider to optimize healthcare systems and interventions in a way that enhances perinatal women’s treatment whenever necessary.

Bina, Rena (ORCID:0000000340729229)↗

Experimentally Inferred Fusion Yield Dependencies of OMEGA Inertial Confinement Fusion Implosions

Statistical modeling of experimental and simulation databases has enabled the development of an accurate predictive capability for deuterium-tritium layered cryogenic implosions at the OMEGA laser. Here, a physics-based statistical mapping framework is described and used to uncover the dependencies of the fusion yield. This model is used to identify and quantify the degradation mechanisms of the fusion yield in direct-drive implosions on OMEGA. The yield is found to be reduced by the ratio of laser beam to target radius, the asymmetry in inferred ion temperatures from the $l$ = 1 mode, the time span over which tritium fuel has decayed, and parameters related to the implosion hydrodynamic stability. When adjusted for tritium decay and $l$ = 1 mode, the highest yield in OMEGA cryogenic implosions is predicted to exceed 2 × 10 14 fusion reactions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

OpenSAMPL: An Open Source Library for Timing and Synchronization Measurements and Analytics

Today's power grid operators are implementing timing and synchronization solutions that provide resilience to Global Navigation Satellite System (GNSS) vulnerabilities. These vendor-specific solutions often come with additional software applications that are designed to monitor that vendor's synchronization performance data. However, resilient timing architectures often resulting in multi-vendor solutions, including approaches that blend terrestrial clocks with space-based subscription services. In such an environment, collecting, analyzing, and visualizing data from a variety of sources within a single platform was heretofore not possible. To address this need, the US Department of Energy's Center for Alternative Synchronization and Timing (CAST) developed OpenSAMPL, the Open Synchronized Analytics and Monitoring Platform, an open-source Python framework for processing, loading, and observing clock measurement data from distributed devices. OpenSAMPL enables the ingestion of diverse clock-probe sources into a scalable time-series database and applies robust analytics. OpenSAMPL currently supports two vendor data pipelines, and will be extended to more in the near future, enabling seamless monitoring of a variety of timing and synchronization devices in a common environment.

Grant, Josh [ORNL] (ORCID:0000000163475060)↗

NASA Interactive Forms Type Interface - NIFTI

A flexible database query, update, modify, and delete tool was developed that provides an easy interface to Oracle forms. This tool - the NASA interactive forms type interface, or NIFTI - features on-the- fly forms creation, forms sharing among users, the capability to query the database from user-entered criteria on forms, traversal of query results, an ability to generate tab-delimited reports, viewing and downloading of reports to the user s workstation, and a hypertext-based help system. NIFTI is a very powerful ad hoc query tool that was developed using C++, X-Windows by a Motif application framework. A unique tool, NIFTI s capabilities appear in no other known commercial-off-the- shelf (COTS) tool, because NIFTI, which can be launched from the user s desktop, is a simple yet very powerful tool with a highly intuitive, easy-to-use graphical user interface (GUI) that will expedite the creation of database query/update forms. NIFTI, therefore, can be used in NASA s International Space Station (ISS) as well as within government and industry - indeed by all users of the widely disseminated Oracle base. And it will provide significant cost savings in the areas of user training and scalability while advancing the art over current COTS browsers. No COTS browser performs all the functions NIFTI does, and NIFTI is easier to use. NIFTI s cost savings are very significant considering the very large database with which it is used and the large user community with varying data requirements it will support. Its ease of use means that personnel unfamiliar with databases (e.g., managers, supervisors, clerks, and others) can develop their own personal reports. For NASA, a tool such as NIFTI was needed to query, update, modify, and make deletions within the ISS vehicle master database (VMDB), a repository of engineering data that includes an indentured parts list and associated resource data (power, thermal, volume, weight, and the like). Since the VMDB is used both as a collection point for data and as a common repository for engineering, integration, and operations teams, a tool such as NIFTI had to be designed that could expedite the creation of database query/update forms which could then be shared among users.

Jain, Bobby↗

Two frameworks for integrating knowledge in induction

The use of knowledge in inductive learning is critical for improving the quality of the concept definitions generated, reducing the number of examples required in order to learn effective concept definitions, and reducing the computation needed to find good concept definitions. Relevant knowledge may come in many forms (such as examples, descriptions, advice, and constraints) and from many sources (such as books, teachers, databases, and scientific instruments). How to extract the relevant knowledge from this plethora of possibilities, and then to integrate it together so as to appropriately affect the induction process is perhaps the key issue at this point in inductive learning. Here the focus is on the integration part of this problem; that is, how induction algorithms can, and do, utilize a range of extracted knowledge. Preliminary work on a transformational framework for defining knowledge-intensive inductive algorithms out of relatively knowledge-free algorithms is described, as is a more tentative problems-space framework that attempts to cover all induction algorithms within a single general approach. These frameworks help to organize what is known about current knowledge-intensive induction algorithms, and to point towards new algorithms.

Rosenbloom, Paul S.↗

Construction of an Anion-Pillared MOF Database and the Screening of MOFs Suitable for Xe/Kr Separation

The separation of xenon/krypton (Xe/Kr) mixtures is a challenging process. Many porous materials allow the adsorption of both Xe and Kr but only with low selectivity. Anion-pillared metal–organic frameworks (MOFs), featuring the anion groups as structural pillars, show potential in gas separations, but only a limited number of them have been synthesized. Here, we describe a collection of 936 anion-pillared MOFs based on 22 experimentally available structures. We performed density functional theory (DFT) optimization and then assigned density-derived electrostatic and chemical (DDEC) charges for each MOF to make them well suited to molecular simulations. The structural properties of the MOFs vary more strongly with the choice of the organic ligand than with other aspects like fluorine groups and metal centers. We then screened the entire collection of MOFs in the context of Xe/Kr separation at room temperature. Compared with previously reported MOFs, the interpenetrated MOF SIFSIX-6-Cd-i is predicted to perform better for Xe/Kr separations, with a good balance between working capacity (1.62 mmol/g) and separation selectivity (16.4) at 298 K and 100 kPa. Here, we also found that the heterogeneity of fluorine groups within a MOF can help to enhance Xe working capacity without reducing the Xe/Kr selectivity, suggesting that synthesis of anion-pillared MOFs with mixed fluorine groups may lead to improved Xe/Kr separation performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment

This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R 2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system’s variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Oak Ridge National Laboratory Pilot Demonstration of an Attestation and Anomaly Detection Framework using Distributed Ledger Technology for Power Grid Infrastructure

This report summarizes the design and pilot demonstration of a framework called Grid Guard that was created to provide increased data and device trustworthiness to electric grid devices by leveraging distributed ledger technology (DLT), specifically blockchain. Grid Guard contains a combination of core cryptographic methods such as the secure hash algorithm (SHA), and asymmetric cryptography, private permissioned blockchain, baselining configuration data, consensus algorithm (Raft) and the Hyperledger Fabric (HLF) framework. The system implements a low energy, fast, and robust enhancement to system trustworthiness within and across electric grid systems such as substations, control centers and metering infrastructures. Blockchain is a distributed database structured that provides a practically unalterable (immutable) timeline of stored transactions. By relying on hashing and the Raft consensus algorithm, if an entity tries to illegitimately alter a record at one instance of the database the other ledger nodes are not altered. They work to cross-reference each other and easily locate any incorrectly added data and remove it. The bulk raw data is stored in an off-chain storage (outside of the blockchain ledger) and a hash of this baseline data is stored in the Blockchain ledger via hashing windows of time-series and configuration data, after aggregation and filtering. The bulk off-chain data repository is then considered to be trust-anchored using the hashes stored in the blockchain. To secure the electric grid testbed devices and data, device configuration baselines were compared to those baselines that had been previously stored in the ledger. Statistical baselines for device configurations, network communication patterns, and high-speed sensor data are calculated and then stored off-chain and hashes stored in the ledger. Measurements such as three-phase voltage and current, frequency, breaker status, protection scheme settings, network configuration settings (and other device configuration artifacts) and network traffic features (packet interarrival times) are compared every minute or other selected time windows. During phase 1 of the Grid Guard DLT project different DLT technologies were studies, and an assessment was performed on DLT technology vulnerabilities, uses, and key characteristics. DLT consensus protocols were studies (e.g., RAFT, named after Reliable, Replicated, Redundant, And Fault-Tolerant). Also, cryptography, public, private and permissioned or permissionless systems were assessed. Grid Guard implements a permissioned private DLT. Consensus algorithm selection and choice of DLT implementation depended heavily on the use-case. For this use-case, parameters were selected to measure performance and existing tools for assessment. Benchmarking was performed theoretically and practically. During phase 2 hashed transactions/blocks were inserted into the ledger every second. During phase 2 of the Grid Guard DLT project, a prototype framework was developed and demonstrated for attestation of critical substation devices and data using precision timing systems that use PTP and IRIG-B protocols) on a testbed of operational devices that emulated a distribution substation, control center, and power metering infrastructure using real Operational Technology (OT). The testbed includes OT devices such as protective relays, human machine interfaces (HMI), and power meters. To determine when to collect and compare system and network baselines, an initial examination of an anomaly detection capability to identify malicious manipulation of data streams was conducted. The resulting anomaly detection was demonstrated in a set of experiments and leveraged to trigger device artifact attestation checks. Attestation checks occur against device configuration baselines when compared with the immutable blockchain-stored baselines, which provided a cryptographically supported means by which to store baselines. The electrical substation-grid testbed was created to test the Grid Guard framework. The testbed emulates the operations of a portion of a power grid and SCADA systems as closely as possible. The testbed integrates real protocols, mainly IEC 61850 standard protocols, such as the Sampled Value (SV) and the GOOSE protocols. The testbed also supports DNP3 and other layer 2 and layer 3 protocols such as Telnet, SSH, SFTP/FTP and other proprietary protocols needed to connect to industrial control system equipment. The testbed emulates real power conditions using the OpalRT hardware-in-the-loop (HIL) device which can create fault situations that cannot be easily tested on real systems. The electrical substation-grid testbed was created using real measurement, communication, and protection devices that electrical utilities commonly use.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Incorporating Flexibility Effects into Metal–Organic Framework Adsorption Simulations Using Different Models

High-throughput calculations based on molecular simulations to predict the adsorption of molecules inside metal–organic frameworks (MOFs) have become a useful complement to experimental efforts to identify promising adsorbents for chemical separations and storage. For computational convenience, all existing efforts of this kind have relied on simulations in which the MOF is approximated as rigid. In this paper, we use extensive adsorption–relaxation simulations that fully include MOF flexibility effects to explore the validity of the rigid framework approximation. We also examine the accuracy of several approximate methods to incorporate framework flexibility that are more computationally efficient than adsorption–relaxation calculations. We first benchmark various models of MOF flexibility for four MOFs with well-established CO 2 experimental consensus isotherms. We then consider a range of adsorption properties, including Henry’s constants, nondilute loadings, and adsorption selectivity, for seven adsorbates in 15 MOFs randomly selected from the CoRE MOF database. Our results indicate that in many MOFs adsorption–relaxation simulations are necessary to make quantitative predictions of adsorption, particularly for adsorption at dilute concentrations, although more standard calculations based on rigid structures can provide useful information. Finally, we investigate whether a correlation exists between the elastic properties of empty MOFs and the importance of including framework flexibility in making accurate predictions of molecular adsorption. Our results did not identify a simple correlation of this type.

36 MATERIALS SCIENCE↗

Prototype crystal structures for exa-AMD framework

This dataset provides a curated collection of initial crystal structure prototypes for the exa-AMD framework, serving as templates for generating hypothetical candidates in multinary materials discovery through elemental substitution and scaling. It includes 36553 ternary and 5254 quaternary unique structures sourced from public databases such as Materials Project, GNoME, OQMD, AFLOW, and NovoMag, formatted in CIF.

exa-AMD↗

Automated ISS Flight Utilities

During my internship at NASA Johnson Space Center, I worked in the Space Radiation Analysis Group (SRAG), where I was tasked with a number of projects focused on the automation of tasks and activities related to the operation of the International Space Station (ISS). As I worked on a number of projects, I have written short sections below to give a description for each, followed by more general remarks on the internship experience. My first project is titled "General Exposure Representation EVADOSE", also known as "GEnEVADOSE". This project involved the design and development of a C++/ ROOT framework focused on radiation exposure for extravehicular activity (EVA) planning for the ISS. The utility helps mission managers plan EVAs by displaying information on the cumulative radiation doses that crew will receive during an EVA as a function of the egress time and duration of the activity. SRAG uses a utility called EVADOSE, employing a model of the space radiation environment in low Earth orbit to predict these doses, as while outside the ISS the astronauts will have less shielding from charged particles such as electrons and protons. However, EVADOSE output is cumbersome to work with, and prior to GEnEVADOSE, querying data and producing graphs of ISS trajectories and cumulative doses versus egress time required manual work in Microsoft Excel. GEnEVADOSE automates all this work, reading in EVADOSE output file(s) along with a plaintext file input by the user providing input parameters. GEnEVADOSE will output a text file containing all the necessary dosimetry for each proposed EVA egress time, for each specified EVADOSE file. It also plots cumulative dose versus egress time and the ISS trajectory, and displays all of this information in an auto-generated presentation made in LaTeX. New features have also been added, such as best-case scenarios (egress times corresponding to the least dose), interpolated curves for trajectories, and the ability to query any time in the EVADES output. As mentioned above, GEnEVADOSE makes extensive use of ROOT version 6, the data analysis framework developed at the European Organization for Nuclear Research (CERN), and the code is written to the C++11 standard (as are the other projects). My second project is the Automated Mission Reference Exposure Utility (AMREU).Unlike GEnEVADOSE, AMREU is a combination of three frameworks written in both Python and C++, also making use of ROOT (and PyROOT). Run as a combination of daily and weekly cron jobs, these macros query the SRAG database system to determine the active ISS missions, and query minute-by-minute radiation dose information from ISS-TEPC (Tissue Equivalent Proportional Counter), one of the radiation detectors onboard the ISS. Using this information, AMREU creates a corrected data set of daily radiation doses, addressing situations where TEPC may be offline or locked up by correcting doses for days with less than 95% live time (the total amount time the instrument acquires data) by averaging the past 7 days. As not all errors may be automatically detectable, AMREU also allows for manual corrections, checking an updated plaintext file each time it runs. With the corrected data, AMREU generates cumulative dose plots for each mission, and uses a Python script to generate a flight note file (.docx format) containing these plots, as well as information sections to be filled in and modified by the space weather environment officers with information specific to the week. AMREU is set up to run without requiring any user input, and it automatically archives old flight notes and information files for missions that are no longer active. My other projects involve cleaning up a large data set from the Charged Particle Directional Spectrometer (CPDS), joining together many different data sets in order to clean up information in SRAG SQL databases, and developing other automated utilities for displaying information on active solar regions, that may be used by the space weather environment officers to monitor solar activity. I consulted my mentor Dr. Ryan Rios and Dr. Kerry Lee for project requirements and added features, and ROOT developer Edmond Offermann for advice on using the ROOT library. I also received advice and feedback from Dr. Janet Barzilla of SRAG, who tested my code. Besides these inputs, I worked independently, writing all of the code by myself. The code for all these projects is documented throughout, and I have attempted to write it in a modular format. Assuming that ROOT is updated accordingly, these codes are also Y2038-compliant (and Y10K-compliant). This allows the code to be easily referenced, modified and possibly repurposed for non-ISS missions in the future, should the necessary inputs exist. These projects have taught me a lot about coding and software design - I have become a much more skilled C++ programmer and ROOT user, and I also learned to code in Python and PyROOT (and its advantages and disadvantages compared to C++/ ROOT). Furthermore, I have learned about space radiation and radiation modeling, topics that greatly interest me as I pursue a degree in physics. Working alongside experimental physicists like Dr. Rios, I have developed a greater understanding and appreciation for experimental science, something I have always leaned towards but to which I lacked significant exposure. My work in SRAG has also given me the invaluable opportunity to witness the work environment for physicists at NASA, and what a career in academia may look like at a government laboratory such as NASA Johnson Space Center. As I continue my studies and look forward to graduate school and a future career, this experience at NASA has given me a meaningful and enjoyable opportunity to put my skills to use and see what my future career path might hold.

Offermann, Jan Tuzlic↗

Building a Prospective LCA Framework to Analyze Emerging Technologies in a Dynamic System Context

NREL's Lifecycle Analysis Integration into Opensource Numerical models (LiAISON) framework computes temporally explicit life cycle impacts and resource uses for specific technologies (foreground) in a dynamic system context (background). LiAISON computes results for a series of environmental mid-points enabling an analysis of prospective tradeoffs of emerging technologies toward 2100. This prospective feature is of critical importance when analyzing present-day emerging technologies whose large-scale impacts during deployment phases will occur in different, future system contexts. LiAISON systematically accounts for dynamic system changes by applying an integrated background of the future energy-economy-land-climate system, generated by exogenous integrated assessment models (IAMs). Using IAM scenarios, LiAISON generates a time-series of life cycle inventory (LCI) databases, which are then used to calculate the impacts per functional unit per time step. This expands current practice of using static, future system assumptions, e.g., a specific grid-mix each year. Further, IAM scenarios are provided in a standardized format of shared-socioeconomic pathways (SSP) and representative concentration pathways (RCP) combinations. These are coherent, regularly, published, and peer-reviewed scenario combinations that establish a reproducible and standardized societal and climate mitigation futures context. They are comparable across IAMs and expand the system boundary of the traditional LCA by including dimensions such as societal and behavioral changes. We apply the framework to assess two emerging Power-to-Hydrogen processes, high temperature electrolysis using solid oxide fuel cell (HT-SOE) and polymer electrolyte membrane electrolysis (PEME). We compare the technologies to a baseline Hydrogen production process via steam methane reforming. Despite the decarbonized electricity systems' beneficial effects on the PtH2 processes' carbon intensities, we find environmental tradeoffs, which require technology improvements via learning-by-doing to be alleviated. Future work via ongoing collaborations will focus on linking the framework to other energy-economy-land-climate models and open-source life cycle inventory databases.

ENERGY PLANNING, POLICY, AND ECONOMY↗

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗

Efficient QAOA Optimization using Directed Restarts and Graph Lookup

Variational Quantum Algorithms (VQA) aim to enhance the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. These algorithms utilize parameterized circuits and classical optimizers to iteratively execute circuits with varying parameters. However, VQA faces computational overheads due to repeated iterations and random restarts. Prior work suggests using basic sub-graphs to transfer parameters for the input graph, reducing optimizer overheads but limiting applicability to structured regular graphs. In real-world applications, random irregular graphs are common, and existing methods are not scalable or practical for such graphs. This paper presents a framework that aims to improve random irregular graphs in VQA. The framework uses graph similarity and important features like total edge counts, average edge counts, and variance. It follows an iterative process to choose basis sub-graphs from a small database and adjust parameters accordingly. Classical optimizers then utilize these parameters to determine when to restart and perform gradient descent. This approach increases the chances of reaching global maximum points.

Wang, Meng↗

Empirical STORM-E Model

Auroral nighttime infrared emission observed by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument onboard the Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite is used to develop an empirical model of geomagnetic storm enhancements to E-region peak electron densities. The empirical model is called STORM-E and will be incorporated into the 2012 release of the International Reference Ionosphere (IRI). The proxy for characterizing the E-region response to geomagnetic forcing is NO+(v) volume emission rates (VER) derived from the TIMED/SABER 4.3 lm channel limb radiance measurements. The storm-time response of the NO+(v) 4.3 lm VER is sensitive to auroral particle precipitation. A statistical database of storm-time to climatological quiet-time ratios of SABER-observed NO+(v) 4.3 lm VER are fit to widely available geomagnetic indices using the theoretical framework of linear impulse-response theory. The STORM-E model provides a dynamic storm-time correction factor to adjust a known quiescent E-region electron density peak concentration for geomagnetic enhancements due to auroral particle precipitation. Part II of this series describes the explicit development of the empirical storm-time correction factor for E-region peak electron densities, and shows comparisons of E-region electron densities between STORM-E predictions and incoherent scatter radar measurements. In this paper, Part I of the series, the efficacy of using SABER-derived NO+(v) VER as a proxy for the E-region response to solar-geomagnetic disturbances is presented. Furthermore, a detailed description of the algorithms and methodologies used to derive NO+(v) VER from SABER 4.3 lm limb emission measurements is given. Finally, an assessment of key uncertainties in retrieving NO+(v) VER is presented

Mertens, Christopher J.↗

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗