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DOE OSTI2022

Efficient hybrid explicit-implicit learning for multiscale problems

Splitting method is a powerful method to handle application problems by splitting physics, scales, domain, and so on. Many splitting algorithms have been designed for efficient temporal discretization. Here, in this paper, our goal is to use temporal splitting concepts in designing machine learning algorithms and, at the same time, help splitting algorithms by incorporating data and speeding them up. We propose a machine learning assisted splitting scheme which improves the efficiency of the scheme meanwhile preserves the accuracy. We consider a recently introduced multiscale splitting algorithms, where the multiscale problem is solved on a coarse grid. To approximate the dynamics, only a few degrees of freedom are solved implicitly, while others explicitly. This splitting concept allows identifying degrees of freedom that need implicit treatment. In this paper, we use this splitting concept in machine learning and propose several strategies. First, the implicit part of the solution can be learned as it is more difficult to solve, while the explicit part can be computed. This provides a speed-up and data incorporation for splitting approaches. Secondly, one can design a hybrid neural network architecture because handling explicit parts requires much fewer communications among neurons and can be done efficiently. Thirdly, one can solve the coarse grid component via PDEs or other approximation methods and construct simpler neural networks for the explicit part of the solutions. We discuss these options and implement one of them by interpreting it as a machine translation task. This interpretation of the splitting scheme successfully enables us using the Transformer since it can perform model reduction for multiple time series and learn the connection between them. We also find that the splitting scheme is a great platform to predict the coarse solution with insufficient information of the target model: the target problem is partially given and we need to solve it through a known problem which approximates the target. Our machine learning model can incorporate and encode the given information from two different problems and then solve the target problems. We conduct four numerical examples and the results show that our method is stable and accurate.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Benzene Ring Knitting Achieved by Ambient-Temperature Dehalogenation via Mechanochemical Ullmann-Type Reductive Coupling

The current approaches capable of affording conjugated porous networks (CPNs) still rely on solution-based coupling reactions promoted by noble metal complexes or Lewis acids, on-surface polymerization conducted in ultrahigh-vacuum environment at very high temperatures (>200 °C), or mechanochemical Scholl-type reactions limited to electron-rich substrates. To develop simple and scalable approaches capable of making CPNs under neat and ambient conditions, herein, a novel and complementary method to the current oxidative Scholl coupling processes is demonstrated to afford CPNs via direct aromatic ring knitting promoted by mechanochemical Ullmann-type reactions. The key to this strategy lies in the dehalogenation of aromatic halides in the presence of Mg involving the formation of Grignard reagent intermediates. Products (Ph-CPN-1) obtained via direct C-C bond formation between 1,2,4,5-tetrabromobenzene (TBB) monomer feature high surface areas together with mesoporous architecture. The versatility of this approach is confirmed by the successful construction of various CPNs via knitting of the corresponding aromatic rings (e.g., pyrene and triphenylene), and even highly crystalline graphite product was obtained. The CPNs exhibit good electrochemical performance as the anode material in lithium-ion batteries (LIBs). Overall, this approach expands the frontiers of CPN synthesis and provides new opportunities to their scalable applications.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Neural entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws

We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from solution trajectories, without requiring any predefined numerical discretization. While recent neural network architectures have successfully integrated classical numerical principles into learned models, most rely on prior knowledge of the governing equations or assume a fixed discretization. Our approach removes this dependency by embedding entropy-stable design principles into the learning process itself, enabling the discovery of physically consistent dynamics in a fully data-driven setting. By jointly learning both the flux function and a corresponding entropy, NESCFN promotes conservation and entropy dissipation, which is critical for long-term stability and fidelity in the system of hyperbolic conservation laws. Furthermore, numerical results demonstrate that the method achieves stability and conservation over extended time horizons and accurately captures shock propagation speeds, even without oracle access to future-time solution profiles in the training data.

Conservative flux form↗

DOE OSTI2025

Hybrid classical-quantum communication networks

Over the past several decades, the proliferation of global classical communication networks has transformed various facets of human society. Concurrently, quantum networking has emerged as a dynamic field of research, driven by its potential applications in distributed quantum computing, quantum sensor networks, and secure communications. This prompts a fundamental question: rather than constructing quantum networks from scratch, can we harness the widely available classical fiber-optic infrastructure to establish hybrid quantum–classical networks? This paper aims to provide a comprehensive review of ongoing research endeavors aimed at integrating quantum communication protocols, such as quantum key distribution, into existing lightwave networks. This approach offers the substantial advantage of reducing implementation costs by allowing classical and quantum communication protocols to share optical fibers, communication hardware, and other network control resources—arguably the most pragmatic solution in the near term. In the long run, classical communication will also reap the rewards of innovative quantum communication technologies, such as quantum memories and repeaters. Accordingly, our vision for the future of the Internet is that of heterogeneous communication networks thoughtfully designed for the seamless support of both classical and quantum communications.

Fiber-optic communication↗

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 OSTI2013

Metal-Organic Frameworks Incorporating Copper-Complexed Rotaxanes

MOFs on the move: A copper-coordinated [2]pseudorotaxanate which reacts with zinc nitrate to form threefold interpenetrated networks retains most of its solution-state chemistry, including its ability to undergo electronic switching of some of the copper(I) ions under redox control.

Coskun, Ali↗

DOE OSTI2017

Conjugacy Analysis of the Finite-Difference Equations of the Method of Characteristics on the Basis of a Linear-Algebraic Formulation

A linear-algebraic form of the equations of the method of characteristics, which is used to approximate the neutron transport equation, is obtained. It is shown on the basis of the obtained linear-algebraic form that the discrete form of the conjugate equation differs from the algebraically discrete problem constructed by linear-algebraic transformations of the discrete form of the normal problem. The reason for the discrepancy lies in the approximation of the volumes of the spatial cells in covering the working region by a network of characteristics. It is shown by means of test calculations that when the network of characteristics is refined the solution of the conjugate transport equation converges to the solution of the algebraically conjugate problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Novel Fabrication of PAA/PVA/Yeast Superabsorbent with Interpenetrating Polymer Network for pH-Dependent Selective Adsorption of Dyes

Traditional superabsorbent polymers have wide application potential as an adsorbent, but the poor physical and mechanical properties limit their further applications. To tentatively overcome this dilemma, a novel poly(acrylic acid)/poly(vinyl alcohol)/yeast superabsorbent polymers (PAA/PVA/yeast SAPs) with interpenetrating polymer networks (IPNs) were fabricated herein via solution polymerization. The mechanical stability tests showed that the resulting products could desirably resist the destruction of shear flow (<5000 rpm) and load pressure (<3 kg). The effects of yeast content, pH, contact time, initial dye concentration and temperature were systematically studied to evaluate their adsorption properties. Consecutive five cycles of adsorption–desorption indicated that their easy regeneration and reusability. More importantly, the PVA/PAA/yeast SAPs displayed brilliant pH-dependent selective adsorption for dyes in dye mixtures. It is believed hereby that the PAA/PVA/yeast SAPs can be expected to be economically and technically feasible for the scalable treatment of dyes wastewater.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Prediction and Computation of Corrosion Rates of A36 Mild Steel in Oilfield Seawater

The parameters which primarily control the corrosion rate and life of steel structures are several and they vary across the different ocean and seawater as well as along the depth. While the effect of single parameter on corrosion behavior is known, the conjoint effects of multiple parameters and the interrelationship among the variables are complex. Millions sets of experiments are required to understand the mechanism of corrosion failure. Statistical modeling such as ANN is one solution that can reduce the number of experimentation. ANN model was developed using 170 sets of experimental data of A35 mild steel in simulated seawater, varying the corrosion influencing parameters SO{sub 4}{sup 2−}, Cl{sup −}, HCO{sub 3}{sup −},CO{sub 3}{sup 2−}, CO{sub 2}, O{sub 2}, pH and temperature as input and the corrosion current as output. About 60% of experimental data were used to train the model, 20% for testing and 20% for validation. The model was developed by programming in Matlab. 80% of the validated data could predict the corrosion rate correctly. Corrosion rates predicted by the ANN model are displayed in 3D graphics which show many interesting phenomenon of the conjoint effects of multiple variables that might throw new ideas of mitigation of corrosion by simply modifying the chemistry of the constituents. The model could predict the corrosion rates of some real systems.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Heavy ion irradiation response of an additively manufactured 316LN stainless steel

Additive manufacturing has become an appealing technique to fabricate three-dimensional metallic materials and components for nuclear reactors. However, response of additively manufactured alloys to high-dose heavy ion irradiations at elevated temperatures is still not well understood. Here, an additively manufactured 316LN austenitic stainless steel with high-density solidification cells was irradiated using 3.5 MeV Fe ion to a peak dose of 220 dpa at 450 °C. Microscopy studies show a lower Frank loop density and smaller size in the additively manufactured sample compared with its cold worked counterpart, and the cellular structures may largely suppress the formation of perfect loops and dislocation networks and reduce the magnitude of solute segregations comparing with high angle grain boundaries. In conclusion, the present work advances the understanding on the high-temperature irradiation response of additively manufactured steels for nuclear reactor applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A physics-constrained deep learning surrogate model of the runaway electron avalanche growth rate

A surrogate model of the runaway electron avalanche growth rate in a magnetic fusion plasma is developed. This is accomplished by employing a physics-informed neural network (PINN) to learn the parametric solution of the adjoint to the relativistic Fokker–Planck equation. The resulting PINN is able to evaluate the runaway probability function across a broad range of parameters in the absence of any synthetic or experimental data. This surrogate of the adjoint relativistic Fokker–Planck equation is then used to infer the avalanche growth rate as a function of the electric field, synchrotron radiation and effective charge. Predictions of the avalanche PINN are compared against first principle calculations of the avalanche growth rate with excellent agreement observed across a broad range of parameters.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Frustrated Solvation Structures Can Enhance Electron Transfer Rates

Polar surfaces can interact strongly with nearby water molecules, leading to the formation of highly ordered interfacial hydration structures. This ordering can lead to frustration in the hydrogen bond network, and, in the presence of solutes, frustrated hydration structures. We study frustration in the hydration of cations when confined between sheets of the water oxidation catalyst manganese dioxide. Frustrated hydration structures are shown to have profound effects on ion-surface electron transfer through the enhancement of energy gap fluctuations beyond those expected from Marcus theory. These fluctuations are accompanied by a concomitant increase in the electron transfer rate in Marcus’s normal regime. We demonstrate the generality of this phenomenon—enhancement of energy gap fluctuations due to frustration—by introducing a charge frustrated XY model, likening the hydration structure of confined cations to topological defects. Here, our findings shed light on recent experiments suggesting that water oxidation rates depend on the cation charge and Mn-oxidation state in these layered transition metal oxide materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Revealing temperature-dependent polymer aggregation in solution with small-angle X-ray scattering

We report that improving the morphology of bulk heterojunction active layers remains a primary challenge for organic photovoltaics (OPVs), and much research has been devoted to achieving this through modifying OPV casting solutions to control film formation and crystallinity. Yet, the solution conformation of conjugated polymers used in OPVs is largely unknown. Here, we report observations of temperature dependent aggregation (TDA) through small-angle X-ray scattering (SAXS) investigations of polymer conformation in chlorobenzene:dichlorobenzene casting solvent as a function of temperature for PffBT4T-2OD, a polymer known to display TDA, and its derivative PffBT3T-2OD which displays significantly reduced TDA. We find that, upon cooling below 80 °C, PffBT4T-2OD forms large crystalline aggregates in solution, while its derivative PffBT3T-2OD forms mostly amorphous aggregates of similar size with some evidence of short-range order. This change in solution aggregation behavior is reflected in the lack of gelation by PffBT3T-2OD upon film deposition by spin coating. Grazing-incidence wide-angle X-ray scattering (GIWAXS) revealed a preferred face-on π–π stacking orientation for PffBT3T-2OD films while PffBT4T-2OD's π–π stacking peak was isotropic. We combine these findings to suggest that the presence of crystalline seed aggregates in PffBT4T-2OD solution quickly form an isotropic crystallite network upon cooling while PffBT3T-2OD's amorphous aggregates more slowly crystallize resulting in improved processability of PffBT3T-2OD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Change of translational-rotational coupling in liquids revealed by field-cycling {sup 1}H NMR

Applying the field-cycling nuclear magnetic resonance technique, the frequency dependence of the {sup 1}H spin-lattice relaxation rate, R{sub 1}(ω)=T{sub 1}{sup −1}(ω), is measured for propylene glycol (PG) which is increasingly diluted with deuterated chloroform. A frequency range of 10 kHz–20 MHz and a broad temperature interval from 220 to about 100 K are covered. The results are compared to those of experiments, where glycerol and o-terphenyl are diluted with their deuterated counter-part. Reflecting intra- as well as intermolecular relaxation, the dispersion curves R{sub 1}(ω,x) (x denotes mole fraction PG) allow to extract the rotational time constant τ{sub rot}(T, x) and the self-diffusion coefficient D(T, x) in a single experiment. The Stokes-Einstein-Debye (SED) relation is tested in terms of the quantity D(T, x) τ{sub rot}(T, x) which provides a measure of an effective hydrodynamic radius or equivalently of the spectral separation of the translational and the rotational relaxation contribution. In contrast to o-terphenyl, glycerol and PG show a spectral separation much larger than suggested by the SED relation. In the case of PG/chloroform mixtures, not only an acceleration of the PG dynamics is observed with increasing dilution but also the spectral separation of rotational and translational relaxation contributions continuously decreases. Finally, following a behavior similar to that of o-terphenyl already at about x = 0.6; i.e., while D(T, x) τ{sub rot}(T, x) in the mixture is essentially temperature independent, it strongly increases with x signaling thus a change of translational-rotational coupling. This directly reflects the dissolution of the hydrogen-bond network and thus a change of solution structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

An adaptive multi-level simulation algorithm for stochastic biological systems

Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, “Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics,” SIAM Multiscale Model. Simul. 10(1), 146–179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the efficiency of our method using a number of examples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Sub-terahertz spectroscopy reveals that proteins influence the properties of water at greater distances than previously detected

The initial purpose of the study is to systematically investigate the solvation properties of different proteins in water solution by terahertz (THz) radiation absorption. Transmission measurements of protein water solutions have been performed using a vector network analyser-driven quasi-optical bench covering the WR-3 waveguide band (0.220–0.325 THz). The following proteins, ranging from low to high molecular weight, were chosen for this study: lysozyme, myoglobin, and bovine serum albumin (BSA). Absorption properties of solutions were studied at different concentrations of proteins ranging from 2 to 100 mg/ml. The concentration-dependent absorption of protein molecules was determined by treating the solution as a two-component model first; then, based on protein absorptivity, the extent of the hydration shell is estimated. Protein molecules are shown to possess a concentration-dependent absorptivity in water solutions. Absorption curves of all three proteins sharply peak towards a dilution-limit that is attributed to the enhanced flexibility of protein and amino acid side chains. An alternative approach to the determination of hydration shell thickness is thereby suggested, based on protein absorptivity. The proposed approach is independent of the absorption of the hydration shell. The derived estimate of hydration shell thickness for each protein supports previous findings that protein-water interaction dynamics extends beyond 2-3 water solvation-layers as predicted by molecular dynamics simulations and other techniques such as NMR, X-ray scattering, and neutron scattering. According to our estimations, the radius of the dynamic hydration shell is 16, 19, and 25 Å, respectively, for lysozyme, myoglobin, and BSA proteins and correlates with the dipole moment of the protein. It is also seen that THz radiation can serve as an initial estimate of the protein hydrophobicity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Narrowing Frequency Probability Density Function for Achieving Minimized Uncertainties in Power Systems Operation – a Stochastic Distribution Control Perspective

With the increased penetration of renewables and other distributed energy resources (DERs), the frequency response in power systems exhibits stochastic nature, where the standard deterministic swing equation should be either represented or transferred into an Ito stochastic differential equation whose solution should be the non-stationary transient behavior of the probability density function (PDF) of the frequency. This transfers the frequency quality control for power systems into a stochastic distribution control problem, where the shape of the frequency distribution, namely the frequency PDF, needs to be directly controlled. In this context, this requires the development of control strategies via controlling the power balance so that the PDF of the frequency is made as narrow as possible whilst centered at its required mean value simple because such a sharp distribution shape means less uncertainties or randomness for the frequency response. In this paper, the formulation of the stochastic swing equation will be made first taking into account of DERs. This will then be followed by the development of stochastic distribution control model that links the power sources with the PDF of the frequency using Fokker Planck Kolmogorov (FPK) equations. A generic constrained optimization problem will be formulated where the cost function is composed of a functional distance between the actual and the desired PDFs of the frequency. A feasible solution using B-spine Neural Networks based stochastic distribution control model will be described. Using the obtained stochastic distribution control model, a set of control algorithms will be described that uses available power sources to shape the PDF of the frequency or to minimize the randomness of the frequency via minimized entropy approach. Future directions will be discussed in the later part of the paper

Swing equation, Power Systems, stchastic distribut↗

DOE OSTI2015

Resistive Network Optimal Power Flow: Uniqueness and Algorithms

The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.

Tan, CW↗