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Rietveld refinement of the structures of 1.0 C-S-H and 1.5 C-S-H
Low-Q region Rietveld analyses were performed on C-S-H synchrotron XRD patterns, using the software MAUD. Two different crystal structures of tobermorite 11 A were used as a starting model: monoclinic ordered Merlino tobermorite, and orthorhombic disordered Hamid tobermorite. Structural modifications were required to adapt the structures to the chemical composition and the different interlayer spacing of the C-S-H samples. Refinement of atomic positions was done by using special constraints called fragments that maintain interatomic distances and orientations within atomic polyhedra. Anisotropic crystallite size refinement showed that C-S-H has a nanocrystalline disordered structure with a preferred direction of elongation of the nanocrystallites in the plane of the Ca interlayer. The quality of the fit showed that the monoclinic structure gives a more adequate representation of C-S-H, whereas the disordered orthorhombic structure can be considered a more realistic model if the lack of long-range order of the silica chain along the c-direction is assumed.
MIRaGE: Design Software for Metamaterials
Metamaterials are artificial optical structures that allow control of light in ways not found in, or offered by, naturally occurring materials. Sandia's Multiscale Inverse Rapid Group-theory for Engineered-metamaterials (MIRaGE) software, which won an R&D100 award in 2019, allows researchers to deterministically design and produce metamaterials with unique characteristics. MIRaGE also provides powerful autonomous optimization techniques for real-world performance in a rigorous, robust, and accurate manner.
Development and validation of a software for simulating γ-γ coincidence emission and detection probabilities
Gamma-gamma coincidence spectrometers have the potential to significantly enhance detection sensitivity for ultra-trace radionuclide measurements. The implementation of these spectrometers, however, is limited by the complexity of acquisition hardware, data processing and quantification. This work reports development of a novel radionuclide quantification software for γ-γ coincidence measurements. For any radionuclide, the software parses the Evaluated Nuclear Structure Data File (ENSDF) database, recursively simulating all possible γ-γ coincidence signatures and their respective emission and detection probabilities. Implemented using Python programming language, the software employs several strategies to boost overall computational performance. Since coincidence-based spectrometers are of notable interest in monitoring compliance for the Comprehensive Nuclear-Test-Ban Treaty (CTBT), the software’s execution was tested for 84 CTBT-relevant radionuclides. To date, the software has been experimentally validated for 15 radionuclides using the Advanced Radionuclide Gamma spectrOmeter (ARGO) at Pacific Northwest National Laboratory, USA (PNNL). Notably, the software can be operated in convergence mode, whereby coincidence detection efficiency’s convergence behavior can help avoid unreliable radionuclide activity estimates. With growing number of coincidence spectrometers worldwide, this paper aims to assist the radiation metrology community in developing similar software for their system.
PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology
PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.
ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning
Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks where each network casts one vote for the predicted molecule, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The ensemble model achieved an accuracy of 76% on hand-drawn hydrocarbons, increasing to 86% if the top 3 predictions were considered.
An operation verification strategy against network attack of DCS in nuclear power plant - 323
Digital Instrumentation and control (I and C) system serves as the 'senses and nervous system' of a nuclear power plant (NPP). It plays an important role in the safe and economic operation in the NPPs. Digital I and C system has a series of advantages compared with analogue ones, but the introduction of the operation system platform, the application software, and the network communication structure are causing security risk to the power plant. Different kinds of procedures have already been adopted against these risks such as the communication isolation, but the security attacks still occur from time to time in the industrial fields around the world. This paper presents two computer security methods based on the thought of software verification and validation with step-by-step operation identification. First, three scenarios of security attack are proposed according to the structure of a distributed control system (DCS). Then a method with two network bypasses and a security computer are adopted to obtain the operation commands, the actuation signal, and the operation feedback in case of the first two scenarios by identifying the consistency of the obtained data. Furthermore, the method is optimized by combining the operation procedure and the camera shot to identify the anomaly of the information flow, thus to improve the effectiveness against the computer security issues. In the end, the influence and feasibility of this method is discussed. (authors)
First-principles study on structural mechanical and thermodynamic properties of HfMoTaTiZr
This study calculates the phase diagram of HfMoTaTiZr using the TCHEA1 database in Thermo-Calc software. Furthermore, it also investigates the structural and mechanical properties of HfMoTaTiZr by applying first-principles calculations. The elastic and other mechanical properties such as Young’s modulus, pugh ratio, hardness, bulk modulus was derived and discussed. These parameters were found to be in good harmony with previously available experimental reports. Vickers hardness of HfMoTaTiZr predicted from the Tians model agrees with the available experimental result. This study also investigates the thermal coefficient of linear expansion and vibrational heat capacity of the HfMoTaTiZr using the Debye–Grüneisen theory.
Neutron diffraction study of a non-strichiometric Ni-Mn-Ga MSM alloy
The structure and chemical order of a Heusler alloy of non-stoichiometric composition Ni-Mn-Ga were studied using constant-wavelength (1.538 ) neutron diffraction at 363K and the diffraction pattern was refined using the FullProf software. At this temperature the structure is austenite (cubic) with Fm-3m space group and lattice constant of a = 5.83913(4) [ ]. The chemical order is of critical importance in these alloys, as Mn becomes antiferromagnetic when the atoms are closer than the radius of the 3d shell. In the studied alloy the refinement of the site occupancy showed that the 4b (Ga site) contained as much as 22% Mn; that significantly alters the distances between the Mn atoms in the crystal and, as a result, also the exchange energy between some of the Mn atoms. Based on the refinement, the composition was determined to be Ni1.91Mn1.29Ga0.8
Assembly and Analysis of Cell-Scale Membrane Envelopes
The march toward exascale computing will enable routine molecular simulation of larger and more complex systems, for example, simulation of entire viral particles, on the scale of approximately billions of atoms-a simulation size commensurate with a small bacterial cell. Anticipating the future hardware capabilities that will enable this type of research and paralleling advances in experimental structural biology, efforts are currently underway to develop software tools, procedures, and workflows for constructing cell-scale structures. Herein, we describe our efforts in developing and implementing an efficient and robust workflow for construction of cell-scale membrane envelopes and embedding membrane proteins into them. A new approach for construction of massive membrane structures that are stable during the simulations is built on implementing a subtractive assembly technique coupled with the development of a structure concatenation tool (fastmerge), which eliminates overlapping elements based on volumetric criteria rather than adding successive molecules to the simulation system. Using this approach, we have constructed two "protocells" consisting of MARTINI coarse-grained beads to represent cellular membranes, one the size of a cellular organelle and another the size of a small bacterial cell. The membrane envelopes constructed here remain whole during the molecular dynamics simulations performed and exhibit water flux only through specific proteins, demonstrating the success of our methodology in creating tight cell-like membrane compartments. Extended simulations of these cell-scale structures highlight the propensity for nonspecific interactions between adjacent membrane proteins leading to the formation of protein microclusters on the cell surface, an insight uniquely enabled by the scale of the simulations. We anticipate that the experiences and best practices presented here will form the basis for the next generation of cell-scale models, which will begin to address the addition of soluble proteins, nucleic acids, and small molecules essential to the function of a cell.
Toward Generalized Tensor Algebra for ab initio Quantum Chemistry Methods
The widespread use of tensor operations in describing electronic structure calculations has motivated the design of software frameworks for productive development of scalable optimized tensor-based electronic structure methods. Whereas prior work focused on Cartesian abstractions for dense tensors, we present an algebra to specify and perform tensor operations on a larger class of block-sparse tensors. We illustrate the use of this framework in expressing real-world computational chemistry calculations beyond the reach of existing frameworks.
P SI4 1.4: Open-source software for high-throughput quantum chemistry
PSI4 is a free and open-source ab initio electronic structure program providing implementations of Hartree–Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient, thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of PSI4 's core functionalities via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSCHEMA data format, facilitating interoperability. We announce a rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.
3-D advanced gas-cooled nuclear reactor fuel channel reconstruction using structure-from-motion - 110
During planned, periodic outages, a selection of fuel channels within the UK fleet of Advanced Gas-cooled Reactor (AGR) cores are inspected using specialist tools which record video footage and other sensory data for each channel which undergoes inspection. Current visualization techniques comprise of manually produced montages by inspection engineers of points of interest (i.e. structural defects) and 2-D panoramic images of the fuel channels automatically produced using bespoke image stitching software. Both techniques however provide limited structural information due to the loss of depth data as a result of the image formation process. By recovering the depth information from the footage, a 3D model could be constructed and subsequently, allow for more accurate profiling of specific defects observed during inspection in addition to obtaining the fuel channels structure using existing footage. This work explores the preliminary application of a 3-D visualization technique known as Structure-from-Motion (SfM) which aims to obtain 3-D information by exploiting image correspondences across multiple viewpoints of the same scene in the RVI footage. This paper investigates the difficulties of applying state-of-the-art SfM to RVI footage and we present new techniques to improve feature correspondence searching in repetitive, nondescript environments. (authors)
Quantitative Analysis Guidance in Safety Software Validation
Quantitative analysis in software development has been a challenging topic over the years in safety software. While qualitative processes for vigorous safety software verification and validation (V and V) have been provided through standard such as IEEE 1012, quantitative analysis for V and V tasks such as correctness, completeness or accuracy evaluation have not been formally defined. In practice, there are quantitative analyses in terms of using software metrics. Many of these software metrics are useful in analyzing the test results. Some of them are used for providing guidance in code evaluation activities e.g. numbers of line of codes not exceeding certain limits, recursive calls are not allowed, etc. However, few can be used for providing quantitative analysis guidance for the V and V tasks such as completeness evaluation. AREVA Federal Services (AFS) has provided services for many nuclear projects in US. Through compliance with IEEE 1012, AFS V and V processes have provided vigorous qualitative processes ensuring quality level of the software products used in the project meeting the required nuclear software level. In addition, quantitative analysis is developed to provide structured validation framework ensuring 'zero defect' in the software products. This paper describes some aspects of the quantitative approaches using a sample safety application. (authors)
Inverse load calculation procedure for offshore wind turbines and application to a 5‐MW wind turbine support structure
Abstract A significant number of wind turbines installed today have reached their designed service life of 20 years, and the number will rise continuously. Most of these turbines promise a more economical performance if they operate for more than 20 years. To assess a continued operation, we have to analyze the load‐bearing capacity of the support structure with respect to site‐specific conditions. Such an analysis requires the comparison of the loads used for the design of the support structure with the actual loads experienced. This publication presents the application of a so‐called inverse load calculation to a 5‐MW wind turbine support structure. The inverse load calculation determines external loads derived from a mechanical description of the support structure and from measured structural responses. Using numerical simulations with the software fast , we investigated the influence of wind‐turbine‐specific effects such as the wind turbine control or the dynamic interaction between the loads and the support structure to the presented inverse load calculation procedure. fast is used to study the inverse calculation of simultaneously acting wind and wave loads, which has not been carried out until now. Furthermore, the application of the inverse load calculation procedure to a real 5‐MW wind turbine support structure is demonstrated. In terms of this practical application, setting up the mechanical system for the support structure using measurement data is discussed. The paper presents results for defined load cases and assesses the accuracy of the inversely derived dynamic loads for both the simulations and the practical application. Copyright © 2017 John Wiley & Sons, Ltd.
High availability through full redundancy of the CMS detector controls system
The CMS detector control system (DCS) is responsible for controlling and monitoring the detector status and for the operation of all CMS sub detectors and infrastructure. This is required to ensure safe and efficient data taking so that high quality physics data can be recorded. The current system architecture is composed of more than 100 servers in order to provide the required processing resources. An optimization of the system software and hardware architecture is under development to ensure redundancy of all the controlled subsystems and to reduce any downtime due to hardware or software failures. The new optimized structure is based mainly on powerful and highly reliable blade servers and makes use of a fully redundant approach, guaranteeing high availability and reliability. The analysis of the requirements, the challenges, the improvements and the optimized system architecture as well as its specific hardware and software solutions are presented.
Integrated System Planning: Emerging Software Requirements in the Power Industry
Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.
An automatically curated first-principles database of ferroelectrics
Ferroelectric materials have technological applications in information storage and electronic devices. The ferroelectric polar phase can be controlled with external fields, chemical substitution and size-effects in bulk and ultrathin film form, providing a platform for future technologies and for exploratory research. In this work, we integrate spin-polarized density functional theory (DFT) calculations, crystal structure databases, symmetry tools, workflow software, and a custom analysis toolkit to build a library of known, previously-proposed, and newly-proposed ferroelectric materials. With our automated workflow, we screen over 67,000 candidate materials from the Materials Project database to generate a dataset of 255 ferroelectric candidates, and propose 126 new ferroelectric materials. We benchmark our results against experimental data and previous first-principles results. The data provided includes atomic structures, output files, and DFT values of band gaps, energies, and the spontaneous polarization for each ferroelectric candidate. Finally, we contribute our workflow and analysis code to the open-source python packages atomate and pymatgen so others can conduct analogous symmetry driven searches for ferroelectrics and related phenomena.