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DOE OSTI2021

Learning Sequential Distribution System Restoration via Graph-Reinforcement Learning

We report a distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, resilient solution to enhance the restoration performance. The restoration problem is formulated to coordinate distribution generators and controllable switches optimally. A model-based control scheme is usually designed to solve this problem, relying on a precise model and resulting in low scalability. To tackle these limitations, this work proposes a graph-reinforcement learning framework for the restoration problem. We link the power system topology with a graph convolutional network, which captures the complex mechanism of network restoration in power networks and understands the mutual interactions among controllable devices. Latent features over graphical power networks produced by graph convolutional layers are exploited to learn the control policy for network restoration using deep reinforcement learning. The solution scalability is guaranteed by modeling distributed generators as agents in a multi-agent environment and a proper pre-training paradigm. Comparative studies on IEEE 123-node and 8500-node test systems demonstrate the performance of the proposed solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Q-VR: System-Level Design for Future Mobile Collaborative Virtual Reality

High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art software-based mobile VR designs cannot fully satisfy the realtime performance requirements due to the highly interactive nature of user's actions and complex environmental constraints during VR execution. Inspired by the unique human visual system effects and the strong correlation between VR motion features and realtime hardware-level information, we propose Q-VR, a novel dynamic collaborative rendering solution via software-hardware co-design for enabling future low-latency high-quality mobile VR. At software-level, Q-VR provides flexible high-level tuning interface to reduce network latency while maintaining user perception. At hardware-level, Q-VR accommodates a wide spectrum of hardware and network conditions across users by effectively leveraging the computing capability of the increasingly powerful VR hardware. Extensive evaluation on real-world games demonstrates that Q-VR can achieve an average end-to-end performance speedup of 3.4x (up to 6.7x) over the traditional local rendering design in commercial VR devices, and a 4.1x frame rate improvement over the state-of-the-art static collaborative rendering.

Xie, Chenhao↗
DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2016

Development of a Severe Accident Analysis Engine Using Approximate Reasoning

In hypothetical severe accident scenarios, nuclear power plant safety is challenged by core degradation and release of fission products. To aid the plant operator and technical support center staff, research has been performed to develop understanding of severe accident phenomenology, assessing their risk and devise measures to mitigate accident consequences (Sehgal, 2011). Nuclear power plant accidents involve highly complex physical and chemical interactions, a large number of safety related systems, and emergency operating procedures (EOP) and severe accident management guidelines (SAMG). Large number of factors with uncertainty and stress can overwhelm the operators. The overall objective of the present research is to develop a methodology and tools that an operator can use to predict severe accident progression and make effective decisions to arrest and manage the accident under large uncertainties. Towards this goal, this study examines a novel approach that brings to bear a combined use of techniques in artificial intelligence (Russel and Norvig, 2010), including Bayesian Network (BN), Answer Set Programming (ASP) and Fuzzy Inference (FI) to achieve faster-than-real-time, yet sufficiently accurate analysis of severe accident progression. Bayesian network (BN) is a type of probabilistic graphical models that can be used to represent knowledge and conditional dependencies about an uncertain realm. It is useful for capturing the causal relationships in a complex system. It also allows prediction via Bayesian inference (Kelly and Smith, 2012). It is composed of nodes and edges, which express random variables and probabilistic relationships between the nodes, respectively. This relationship, given by conditional probability is calculated using stochastic methods. The BN is used in several systems for analysis and management of accident risk; e.g. (Knochenhauer and Frid, 2010). In those systems, the nodes or so-called 'effective factors' are presumably known within the network by system descriptions in SAMGs. There are two steps to configure the BN, namely (i) identifying compositions within the network and (ii) defining conditional dependency between nodes. The first step is performed following the spirit of ASP (Baral, 2003) using Prolog (Wielemaker, 2005). Knowledge representation is used to build databases required for processing by ASP. Knowledge representation is defined as the expression of knowledge in a way that is understandable to both human and computers. Originally knowledge indicates data, information and facts. To focus, knowledge should be formed to achieve a goal. In a severe accident, key physics and safety related components status are defined according to a failure mode. The conditional dependencies between nodes are generated by approximate reasoning using Fuzzy inference (Zadeh, 1995). Fuzzy logic is a multiple-valued logic to treat ambiguous features such as linguistic description. It indicates possibility, which is distinguished from probability. Fuzzy set consists of linguistic variables according to the fixed rule. In other words, even uncertain states could be expressed as not binary logic, but as many-value logic by rule-based technology. Fuzzy control system is comprised of Fuzzy inference engine that is performed based on the 'If-Then Rule'. The 'If-Then Rule' is of the following form. If Y1 is B11 and Y2 is B12 then Z is C1. If Y1 is B21 and Y2 is B22 then Z is C2. Y1 and Y2 are inputs, which indicate conditional variables. Z indicates decision-making and both B and C are linguistic variables. With this rule-based technique, the approximate reasoning can be realized by Fuzzy inference. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

A graphical user interface platform to support power electronic converters and integrated systems

Power Electronic Systems have grown in size and complexity over the past years. Therefore, users require interfaces to properly configure, develop, and deploy these systems. This work presents a Graphical User Interface that dynamically adapts to the system and allows the user to control and monitor its operation. This GUI uses networked communication and open-source Python libraries to allow the user to interact with the system at its different hierarchical layers, as well as visualize the interconnection of the full system. This is demonstrated by showing the user interface applied to a megawatt class charging station that consists of various energy resources.

Dean, Ben↗
DOE OSTI2015

Selectively catalytic micro- and nanocrystals of metal–organic framework [Co(4-bpdh)(HIA)]{sub ∝}

Metal–organic framework micro-crystals [Co(4-bpdh)(HIA)]{sub ∝} (1Co), crystallized in the monoclinic system (space group P12/n1, a=10.0009(16) Å, b=15.472(3) Å, c=18.563(3) Å, β=91.81(0)°, and Z=4), were controllably synthesized through the adjustment of the solvent system. The Co{sup 2+} ion center located in a six-coordinated environment combined with 5-hydroxyisophthalic acid (HIA) to produce ladder-like structures, which again linked with 2,5-bis(4-pyridyl)-3,4-diaza-2,4-hxadiene (4-bpdh) to yield a double-layer network. Micro-crystals 1Co exhibited selectively oxidation-catalytic properties for the degradation of methyl orange (conversion 90%) owing to ligand to metal charge transfer. Our work determined that the synthesized catalyst is not only highly selective for degradation of organic dyes, but also very efficient. - Graphical abstract: Metal–organic framework micro-crystals [Co(4-bpdh)(HIA)]{sub ∝} (1Co) were controllably synthesized through the adjustment of the solvent system. The Co{sup 2+} ion center located in a six-coordinated environment combined with the ligands to yield a double-layer network. Micro-crystals 1Co exhibited selectively catalytic properties for the degradation of methyl orange (conversion 90%) owing to ligand to metal charge transfer. - Highlights: • Synthesis and structure of metal–organic framework [Co(4-bpdh)(HIA)]{sub ∝}. • Selectively catalytic micro- and nanocrystals. • The degradation and conversion of methyl orange.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

N-donor co-ligands driven two new Co(II)- coordination polymers with bi- and trinuclear units: Crystal structures, and magnetic properties

Two new Co(II) coordination polymers(CPs), namely [Co{sub 2}(bpe){sub 2}(Hbppc)]{sub n} (1) and [Co{sub 3}(μ{sub 3}-OH)(bppc)(bpm)(H{sub 2}O)]·3H{sub 2}O (2) (H{sub 5}bppc=biphenyl-2,4,6,3′,5′-pentacarboxylic acid, bpe=1,2-bis(4-pyridyl)ethene, bpm=bis(4-pyridyl)amine), have been obtained and characterized by elemental analysis, single-crystal X-ray diffraction, powder X-ray diffraction (PXRD), IR spectra and thermogravimetric analysis (TGA). 1 shows a binodal (4,6)-connected fsc net with a (4{sup 4}·6{sup 10}·8)(4{sup 4}·6{sup 2}) topology, while 2 shows a binodal (5,7)-connected 3D network based on trinuclear [Co{sub 3}(μ{sub 3}-OH)]{sup 5+} units with unusual (3.4{sup 6}.5{sup 2}.6)(3{sup 2}.4{sup 6}.5{sup 7}.6{sup 5}.7) topology. Variable-temperature magnetic susceptibility measurements reveals that complex 1 shows ferromagnetic interactions between the adjacent Co(II) ions, whereas 2 is a antiferromagnetic system. - Graphical abstract: Two new Co(II) coordination polymers with bi- and trinuclear units have been obtained. 1 shows a binodal (4,6)-connected fsc net with a (4{sup 4}·6{sup 10}·8)(4{sup 4}·6{sup 2}) topology and antiferromagnetic interactions between the adjacent Co(II) ions, while 2 is a binodal (5,7)-connected 3D network with unusual (3.4{sup 6}.5{sup 2}.6)(3{sup 2}.4{sup 6}.5{sup 7}.6{sup 5}.7) topology and a ferromagnetic system. - Highlights: • Two Co(II) coordination polymers with different multimetallic clusters as building units. • A (4,6)-connected fsc net and a (5,7)-connected 3D network. • A antiferromagnetic coupling for 1 and A ferromagnetic coupling for 2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

The new silver borate Ag{sub 3}B{sub 5}O{sub 9}

Single crystals of Ag{sub 3}B{sub 5}O{sub 9} were obtained via high-pressure synthesis at 3 GPa and 600 °C, using a Walker-type multianvil high-pressure device. Ag{sub 3}B{sub 5}O{sub 9} crystalizes with a=674.7(2), b=943.5(2), c=1103.5(2) pm, V=0.7025(2) nm{sup 3}, and Z=4 in the noncentrosymmetric space group P2{sub 1}2{sub 1}2{sub 1} (no. 19). The orthorhombic structure was refined from 3740 independent reflections with R1=0.0496 and wR2=0.587 (all data). It is built up from infinite corner-sharing chains of BO{sub 4} tetrahedra along the a axis, which are interconnected by BO{sub 3} groups to form a network. In the structure, three crystallographically independent sites are occupied with Ag{sup +} cations exhibiting argentophillic interactions. The synthetic conditions as well as the results of the single crystal structure analysis are presented. - Graphical abstract: Noncentrosymmetric silver borate: During investigations in the system Ag–B–O, a new noncentrosymmetric silver borate Ag{sub 3}B{sub 5}O{sub 9} was discovered. The new structure type is built up from corner-sharing BO{sub 3} and BO{sub 4} groups, forming a network. Argentophillic interactions are clearly indicated by the Ag{sup +}⋯Ag{sup +} distances present in the structure. - Highlights: • A noncentrosymmetric borate Ag{sub 3}B{sub 5}O{sub 9} is accessible via high-pressure synthesis. • Ag{sub 3}B{sub 5}O{sub 9} is the second high-pressure silver borate. • Ag{sup +}⋯Ag{sup +} distances in Ag3B5O9 clearly indicate the presence of argentophillic interactions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings

Recently, there has been considerable research interest in the potential for DC distribution systems in buildings instead of the traditional AC distribution systems. Due to the need for performing power conversions between DC and AC electricity, DC distribution may provide electrical efficiency advantages in some systems. To support comparative evaluations of AC-only, DC-only, and hybrid AC/DC distribution systems in buildings, a new modeling toolkit called the Building Electrical Efficiency Analysis Model (BEEAM) was developed and is described in this paper. To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow, along with nonlinear device behavioral descriptions derived from empirical measurements. This paper describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior and device and system efficiencies using the toolkit are compared with experimental measurements of a small office environment in a variety of operating and load configurations. A detailed analysis of uncertainty estimation is also provided. Key findings were that a comparison of predicted versus measured efficiencies and power losses in the validation testbed using the initial toolkit implementation predicted device- and system-level efficiencies with reasonably good accuracy under both balanced and unbalanced AC scenarios. An uncertainty analysis also revealed that the maximum estimated error for system efficiency across all scenarios was 3%, and measured and modeled system efficiency agreed within the experimental uncertainty in approximately half of the scenarios. Based on the correspondence between simulation and measurement, the toolkit is proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC, and hybrid AC/DC distribution systems in buildings.

DC distribution↗
DOE OSTI2022

Connected and automated vehicles, driving systems, and control logic for info-rich eco-autonomous driving

A method for controlling automated driving operations of a vehicle includes determining vehicle origin and destination data, and generating a graphical representation of a road network with multiple candidate routes between the vehicle's origin and destination. Road-level data, including speed, turn angle, and/or gradient data, is received for each candidate route, and respective total energy uses are estimated for the vehicle to traverse across the candidate routes. Multiple candidate driving strategies, each having respective speed and acceleration profiles, are determined for the candidate route with the lowest estimated total energy use. An optimal candidate driving strategy is selected through a cost evaluation of the associated speed and acceleration profiles and forward movement simulations of the vehicle over a prediction horizon. Command signals are transmitted to the vehicle's steering and/or powertrain systems to execute control operations based on the optimal driving strategy and the candidate route with the lowest energy use.

Zhao, Junfeng↗
DOE OSTI2015

Metal–organic coordination architectures of tetrazole heterocycle ligands bearing acetate groups: Synthesis, characterization and magnetic properties

Two new coordination complexes with tetrazole heterocycle ligands bearing acetate groups, [Co(L){sub 2}]{sub n} (1) and [Co{sub 3}(L){sub 4}(N{sub 3}){sub 2}·2MeOH]{sub n} (2) (L=tetrazole-1-acetate) have been synthesized and structurally characterized. Single crystal structure analysis shows that the cobalt-complex 1 has the 3D 3,6-connected (4{sup 2}.6){sub 2}(4{sup 4}.6{sup 2}.8{sup 8}.10)-ant topology. By introducing azide in this system, complex 2 forms the 2D network containing the [Co{sub 3}] units. And the magnetic properties of 1 and 2 have been studied. - Graphical abstract: The synthesis, crystal structure, and magnetic properties of the new coordination complexes with tetrazole heterocycle ligands bearing acetate groups are reported. - Highlights: • Two novel Cobalt(II) complexes with tetrazole acetate ligands were synthesized. • The magnetic properties of two complexes were studied. • Azide as co-ligand resulted in different structures and magnetic properties. • The new coordination mode of tetrazole acetate ligand was obtained.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Syntgen: a system to generate temporal networks with user-specified topology

Abstract In the last few years, the study of temporal networks has progressed markedly. The evolution of clusters of nodes (or communities) is one of the major focus of these studies. However, the time dimension increases complexity, introducing new constructs and requiring novel and enhanced algorithms. In spite of recent improvements, the relative scarcity of timestamped representations of empiric networks, with known ground truth, hinders algorithm validation. A few approaches have been proposed to generate synthetic temporal networks that conform to static topological specifications while in general adopting an ad hoc approach to temporal evolution. We believe there is still a need for a principled synthetic network generator that conforms to problem domain topological specifications from a static as well as temporal perspective. Here, we present such a system. The unique attributes of our system include accepting arbitrary node degree and cluster size distributions and temporal evolution under user control, while supporting tunable joint distribution and temporal correlation of node degrees. Theoretical contributions include the analysis of conditions for graphic sequences of inter- and intracluster node degrees and cluster sizes and the development of a heuristic to search for the cluster membership of nodes that minimizes the shared information distance between clusterings. Our work shows that this system is capable of generating networks under user controlled topology with up to thousands of nodes and hundreds of clusters with strong topology adherence. Much larger networks are possible with relaxed requirements. The generated networks support algorithm validation as well as problem domain analysis.

Pereira, Luis Ramada↗
DOE OSTI2025

Entanglement swapping systems toward a quantum internet

We demonstrate conditional entanglement swapping, i.e. teleportation of entanglement, between time-bin qubits at the telecommunication wavelength of 1536.4 nm with high fidelity of 87%. Our system is deployable, utilizing modular, off-the-shelf, fiber-coupled, and electrically controlled components such as electro-optic modulators. It leverages the precise timing resolution of superconducting nanowire detectors, which are controlled and read out via a custom developed graphical user interface. The swapping process is described, interpreted, and guided using characteristic function-based analytical modeling that accounts for realistic imperfections. Our system supports quantum networking protocols, including source-independent quantum key distribution, with an estimated secret key rate of approximately 0.5 bits per sifted bit.

Davis, Samantha I. [Caltech] (ORCID:00000001999481↗
DOE OSTI2023

Risk-Informed Condition Evaluation of Solar-centered Energy Generation and Distribution Networks through Bayesian Learning and Inference

We develop a methodology based on Bayesian inference over Probabilistic Graphical Models (PGMs) to understand and quantify risk in solar-centered grids using targeted measurements and learned system behavior. Being non-prescriptive but, rather, able to infer system behavior and, ultimately, address risk queries from data, our machine learning-type paradigm is tailored for diverse topologies and threat scenarios often associated with distributed energy generation and photovoltaic distributed energy resources (PV-DERs) in particular. We describe algorithmic processes for: (i) learning the structure of PGMs that result from attack-prone PV-DER-proliferated distribution systems, (ii) quantifying cause-effect relationships, and (iii) evaluating risk queries based on diverse evidence. The contributions are illustrated on a residential grid subject to output impairment attacks on its PV-DER infrastructure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Neural Networks and Graph Algorithms with Next-Generation Processors

The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.

Hamilton, Kathleen↗
DOE OSTI2015

Explicit integration with GPU acceleration for large kinetic networks

We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. This orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems in various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Synthesis, structure and optical properties of two isotypic crystals, Na{sub 3}MO{sub 4}Cl (M=W, Mo)

Two isotypic compounds, Na{sub 3}MO{sub 4}Cl (M = W, Mo) have been obtained from the high temperature solution, and their structures were determined by single-crystal X-ray diffraction. Both of them crystallize in the space group P4/nmm of tetragonal system with the unit cells: a=7.5181(15), c=5.360(2) for Na{sub 3}WO{sub 4}Cl and a=7.4942(12), c=5.3409(18) for Na{sub 3}MoO{sub 4}Cl. The structure exhibits a 3D network built up by the ClNa{sub 6} groups, and the MO{sub 4} groups reside in the tunnels of the 3D network. The structural similarities and differences between Na{sub 3}MO{sub 4}Cl (M=W, Mo) and Sr{sub 3}MO{sub 4}F (M=Al, Ga) have been discussed. Meanwhile, detailed structure comparison analyses between Na{sub 3}MO{sub 4}Cl (M=W, Mo) and Na{sub 3}MO{sub 4}F (M=W, Mo) indicate that the different connection modes of ClNa{sub 6} and FNa{sub 6} make Na{sub 3}MO{sub 4}Cl and Na{sub 3}MO{sub 4}F crystallize in different structures. The IR spectra were used to verify the validity of the structure. The diffuse reflectance spectra show that the UV absorption edges are about 249 nm (4.99 eV) and 265 nm (4.69 eV) for Na{sub 3}WO{sub 4}Cl and Na{sub 3}MoO{sub 4}Cl, respectively. In addition, the first-principles theoretical studies are also carried out to aid the understanding of electronic structures and linear optical properties. - Graphical abstract: Two isotypic compounds, Na{sub 3}MO{sub 4}Cl (M=W, Mo) have been obtained from the high temperature solution. Both of them crystallize in the space group P4/nmm of tetragonal system. The structure exhibits a 3D network built up by the ClNa{sub 6} groups, and the MO{sub 4} groups reside in the tunnels of the 3D network. - Highlights: • Structure and properties of Na{sub 3}MO{sub 4}Cl (M=W, Mo) are reported for the first time. • They show a 3D network built by ClNa{sub 6}, and WO{sub 4} lies in the tunnels of the network. • IR spectra were used to verify the validity of the structure. • Band structures and density of states have been calculated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗