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DOE OSTI2022

Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation

The combination of neural networks and quantum Monte Carlo methods has arisen as a promising path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with a final antisymmetric layer in order to satisfy the antisymmetry requirements of the electronic wavefunction. However, to date it is unclear if one can represent antisymmetric functions of physical interest, and it is difficult to precisely measure the expressiveness of the antisymmetric layer. Here, this work attempts to address this problem by introducing explicitly antisymmetrized universal neural network layers. This approach has a computational cost which increases factorially with respect to the system size, but we are nonetheless able to apply it to small systems to better understand how the structure of the antisymmetric layer affects its performance. We first introduce a generic antisymmetric (GA) neural network layer, which we use to replace the entire antisymmetric layer of the highly accurate ansatz known as the FermiNet. We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small atoms and molecules. We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing the products of determinants with products of antisymmetrized neural networks. We find, interestingly, that the resulting FermiNet-FA architecture does not significantly outperform the FermiNet. This strongly suggests that the sum of products of antisymmetries is a key limiting aspect of the FermiNet architecture. To explore this further, we investigate a slight modification of the FermiNet, called the full determinant mode, which replaces each product of determinants with a single combined determinant. We find that the full single-determinant FermiNet closes a large part of the gap between the standard single-determinant FermiNet and FermiNet-GA on small atomic and molecular problems. Surprisingly, on the nitrogen molecule at a dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can outperform the largest standard FermiNet calculation with 64 determinants.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

A differentiable approach to the maximum independent set problem using dataless neural networks

The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learning by leveraging datasets of the combinatorial structures of interest drawn from some distribution of problem instances. Reinforcement learning has also been employed to find such structures. Here, in this paper, we propose a different approach in that no data is required for training the neural networks that produce the solution. In this sense, what we present is not a machine learning solution, but rather one that is dependent on neural networks and where backpropagation is applied to a loss function defined by the structure of the neural network architecture as opposed to a training dataset. In particular, we reduce the popular combinatorial optimization problem of finding a maximum independent set to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest. Additionally, we propose a universal graph reduction procedure to handle large-scale graphs. The reduction exploits community detection for graph partitioning and is applicable to any graph type and/or density. Experimental results on both real and synthetic graphs demonstrate that our proposed method performs on par or outperforms state-of-the-art learning-based methods in terms of the size of the found set without requiring any training data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A scalable algorithm for the optimization of neural network architectures

In this work, we propose a new scalable method to optimize the architecture of an artificial neural network. The proposed algorithm, called Greedy Search for Neural Network Architecture, aims to determine a neural network with minimal number of layers that is at least as performant as neural networks of the same structure identified by other hyperparameter search algorithms in terms of accuracy and computational cost. Numerical results performed on benchmark datasets show that, for these datasets, our method outperforms state-of-the-art hyperparameter optimization algorithms in terms of attainable predictive performance by the selected neural network architecture, and time-to-solution for the hyperparameter optimization to complete.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Creation of Self-Semi-Interpenetrating Network Structures in PIM-1 Membranes for Enhanced Physical Aging Resistance

A series of self-semi-interpenetrating network (ssIPN) thin films based on PIM-1 structure were developed by end-cross-linking telechelic PIM-1 oligomers end-capped with curable carboxylic acid groups to form model networks, which are penetrated by linear high-molecular-weight PIM-1 chains. ssIPN films with systematically varied network content ranging from 10 to 30 wt % were comprehensively examined on their microstructure and gas permeation properties. Fresh PIM-1 ssIPN films exhibited gas separation performances close to those of as-cast linear PIM-1, where the films closely followed the upper bound trade-off line, gaining as much selectivity as they lose gas permeability as the network content increases. This indicates that gas permeability is largely preserved despite cross-linking. Wide-angle X-ray scattering supported this observation, with peaks shifting toward lower d-spacing as oligomer content increased, suggesting tighter chain packing with higher cross-linkable oligomer loading. In physical aging studies over two months, all ssIPN films outperform linear PIM-1 for H 2 /CH 4 and O 2 /N 2 separations. Notably, the PIM-1 ssIPN with 10 wt % network content showed the best physical aging resistance, with negligible permeability loss even after one month. Finally, these findings highlight a promising macromolecular strategy for enhancing the physical aging resistance of microporous polymer membranes for gas separation.

PIM-1↗

DOE OSTI2025

Degrees of Rate Control in Interconnected Reaction Networks

Overall reactions in interconnected networks exhibit net, forward, and reverse rates that are governed by both constitutive elementary steps in the pathway of interest and branching elementary steps that lead to alternative products. Accordingly, steps in branching pathways exhibit negative net, forward, and reverse degrees of rate control, as they reduce reaction flux to the desired product. We here contextualize the forward and reverse degrees of rate control in terms of kinetic resistances (inverse of rates) and leverage the additive nature of kinetic resistance to decouple kinetic driving forces contributed by constitutive elementary steps and branching points (nodal species) in interconnected networks. Regardless of the network connectivity, forward and reverse degrees of rate control are shown to converge at equilibrium. Away from equilibrium, we identify two critical features of interconnected networks: stoichiometric regularity─condition where all stoichiometric numbers are unity─and pathway symmetry around nodal species─condition where branching pathways share the same rate constants, stoichiometry, and species concentrations/activities─that result in (i) equal forward, reverse, and consequently net degrees of rate control and (ii) forward and reverse degrees of rate control that exhibit constant offsets, respectively, across all extents of reaction. Furthermore, our discourse further provides a mathematical description for the influence of stoichiometric irregularity and pathway asymmetry on forward and reverse degrees of rate control. Altogether, the presented work details the effects of network (inter)connectivity and stoichiometry on reaction kinetics and, in doing so, establishes general protocols for capturing these effects as additive terms in the formulation of forward and reverse degrees of rate control.

10 SYNTHETIC FUELS↗

DOE OSTI2022

Interplay of River and Tidal Forcings Promotes Loops in Coastal Channel Networks

We report the dense populations that inhabit global coastlines have an uncertain future due to increased flooding, storms, and human modification. The channel networks of deltas and marshes that plumb these coastlines present diverse architectures, including well-studied dendritic topologies. However, the quasi-stable loops that exist in nearly all coastal networks have not yet been explained. We present a model for self-organizing networks inspired by vascular biophysics to show that loops emerge when the relative forcings between rivers and tides are comparable, resulting in interplay between hydrodynamic forcings at short time scales relative to network evolution. Using field data and satellite imaging, we confirm this control on 21 field networks. Our comparison provides compelling evidence that hydrodynamic fluctuations are capable of stabilizing loops in geophysical systems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Reactive Transport Modeling of Mineral Precipitation and Carbon Trapping in Discrete Fracture Networks

In this study we use numerical experiments to analyze reactive flow and transport behavior in discrete intersecting fracture networks, focusing on (a) how reaction-induced changes in physical and chemical properties affect flow connectivity and (b) how fracture networks developed in the Earth's critical zone contribute to carbon sequestration via mineral weathering reactions. In the first part of the study, we used two-dimensional reactive flow and transport simulations to analyze the impacts of mixing in a natural discrete fracture network. We concluded that reaction-induced changes can substantially alter the flow connectivity, especially at fracture intersections. The second set of simulations considered the problem of natural weathering of fractured mafic and ultramafic rocks in the partially saturated Earth's critical zone as a function of infiltration rates, fracture permeability, and partially saturated flow parameters. As a model system, we considered an incongruent reaction network with dissolution of forsterite and precipitation of magnesite. The behavior is complex in terms of the rate-controlling processes because of the multicomponent nature of the system as shown by the grid Peclet number: the CO 2 behavior is gas diffusion-controlled in the partially saturated zone, while the rate of water flow via the Damkӧhler number controls Mg 2+ transport through the fracture network. The amounts of carbon that can be trapped are modest, but the naturally fractured domain considered here provides a useful “base case” against which various engineered solutions can be compared.

58 GEOSCIENCES↗

DOE OSTI2022

A Dynamic Pore Network Model for Imbibition Simulation Considering Corner Film Flow

Wetting films can develop in the corners of angular pores under strong wetting conditions. Modeling the dynamics of corner film remains elusive using direct numerical simulations because of the significant scale difference between main meniscus and corner film flow. In this paper, the modified interacting capillary bundle model (ICB), developed in our previous work to describe accurately corner film dynamics in a single square tube, is incorporated into a single-pressure dynamic pore network model (DPNM) to simulate imbibition in strongly wetting porous media with corner film flow. The traditional pore network is decomposed into several layers of interacting subpore networks where the 0th layer of subpore network simulates the main meniscus flow and higher layers the corner film flow. The fluid flow between different layers is captured by interlayer throats. In addition, the snap-off mechanism caused by the thickening of wetting corner film is considered. The accuracy of the developed model is validated for four cases: spontaneous imbibition in a single square tube, wetting fluid redistribution through corner films under a capillary pressure difference, snap off in a narrow throat connecting two large pores, and imbibition dynamics in a real microfluidic porous geometry. The validated model is then used to simulate both spontaneous and controlled imbibition in a pore network with random pore size distribution. Finally, the interaction between corner film and main meniscus flow in porous media is analyzed from a pore-scale perspective.

58 GEOSCIENCES↗

DOE OSTI2023

Effects of Dead‐End Fractures on Non‐Fickian Transport in Three‐Dimensional Discrete Fracture Networks

Abstract Understanding mechanistic causes of non‐Fickian transport in fractured media is important for many hydrogeologic processes and subsurface applications. This study elucidates the effects of dead‐end fractures on non‐Fickian transport in three‐dimensional (3D) fracture networks. Although dead‐end fractures have been identified as low‐velocity regions that could delay solute transport, the direct relation between dead‐end fractures and non‐Fickian transport has been elusive. We systematically generate a large number of 3D discrete fracture networks with different fracture length distributions and fracture densities. We then identify dead‐end fractures using a novel graph‐based method. The effect of dead‐end fractures on solute residence time maximizes at the critical fracture density of the percolation threshold, leading to strong late‐time tailing. As fracture density increases beyond the percolation threshold, the network connectivity increases, and dead‐end fractures diminish. Consequently, the increase in network connectivity leads to a reduction in the degree of late‐time tailing. We also show that dead‐end fractures can inform about main transport paths, such as the mean tortuosity of particle trajectories. This study advances our mechanistic understanding of solute transport in 3D fracture networks.

58 GEOSCIENCES↗

DOE OSTI2020

Engineering regulatory networks for complex phenotypes in E. coli

Regulatory networks describe the hierarchical relationship between transcription factors, associated proteins, and their target genes. Regulatory networks respond to environmental and genetic perturbations by reprogramming cellular metabolism. Here we design, construct, and map a comprehensive regulatory network library containing 110,120 specific mutations in 82 regulators expected to perturb metabolism. We screen the library for different targeted phenotypes, and identify mutants that confer strong resistance to various inhibitors, and/or enhanced production of target compounds. These improvements are identified in a single round of selection, showing that the regulatory network library is universally applicable and is convenient and effective for engineering targeted phenotypes. The facile construction and mapping of the regulatory network library provides a path for developing a more detailed understanding of global regulation in E. coli , with potential for adaptation and use in less-understood organisms, expanding toolkits for future strain engineering, synthetic biology, and broader efforts.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Network Analysis of Academic Medical Center Websites in the United States

Healthcare resources are published annually in repositories such as the AHA Annual Survey Database TM . However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Assessing DER Network Cybersecurity Defences in a Power-Communication Co-Simulation Environment

Increasing penetrations of interoperable distributed energy resources (DER) in the electric power system are expanding the power system attack surface. Maloperation or malicious control of DER equipment can now cause substantial disturbances to grid operations. Fortunately, many options exist to defend and limit adversary impact on these newly-created DER communication networks, which typically traverse the public internet. However, implementing these security features will increase communication latency, thereby adversely impacting real-time DER grid support service effectiveness. In this work, a collection of software tools called SCEPTRE were used to create a co-simulation environment where SunSpec-compliant PV inverters were deployed as virtual machines and interconnected to simulated communication network equipment. Network segmentation, encryption, and moving target defence security features were deployed on the control network to evaluate their influence on cybersecurity metrics and power system performance. The results indicated that adding these security features did not impact DER-based grid control systems but improved the cybersecurity posture of the network when implemented appropriately.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Three-Dimensional Network Adjustment of Laser Tracker Measurements for Large-Scale Metrology Applications

The adjustment of laser tracker network measurements for large-scale metrology applications has unique considerations not commonly encountered in the network adjustment approaches generally used in either standard metrology or in traditional geodetic surveying. Many applications that require the measurement accuracy of a laser tracker are confined to small working areas to measure the dimensional quality of manufactured parts. Large-scale metrology applications require the same high measurement accuracy, but over a comparatively larger working area. This paper describes a unified least-squares approach to adjust three-dimensional survey network measurements that takes into account the unique considerations of large-scale metrology networks. The impetus for these unique considerations arises from an application that falls neatly between the disciplines of metrology and geodesy—resembling a mixture of the conditions and requirements from both. In conclusion, this paper will also describe a case study of a large-scale metrology survey network used to align the particle accelerator at Michigan State University’s Facility for Rare Isotope Beams (FRIB).

42 ENGINEERING↗

DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper we discuss recent advances in deep convolutional neural networks (CNN) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory (LSTM) networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F 1 -score of ~91% on individual time-slices using only the ECEi data. This indicates the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales not only for mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including impact of recent upgrades to the ECEi diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A multisite decomposition of the tensor network path integrals

Tensor network decompositions of path integrals for simulating open quantum systems have recently been proven to be useful. However, these methods scale exponentially with the system size. This makes it challenging to simulate the non-equilibrium dynamics of extended quantum systems coupled with local dissipative environments. In this work, we extend the tensor network path integral (TNPI) framework to efficiently simulate such extended systems. The Feynman–Vernon influence functional is a popular approach used to account for the effect of environments on the dynamics of the system. In order to facilitate the incorporation of the influence functional into a multisite framework (MS-TNPI), we combine a matrix product state (MPS) decomposition of the reduced density tensor of the system along the sites with a corresponding tensor network representation of the time axis to construct an efficient 2D tensor network. The 2D MS-TNPI network, when contracted, yields the time-dependent reduced density tensor of the extended system as an MPS. The algorithm presented is independent of the system Hamiltonian. We outline an iteration scheme to take the simulation beyond the non-Markovian memory introduced by solvents. Applications to spin chains coupled to local harmonic baths are presented; we consider the Ising, XXZ, and Heisenberg models, demonstrating that the presence of local environments can often dissipate the entanglement between the sites. We discuss three factors causing the system to transition from a coherent oscillatory dynamics to a fully incoherent dynamics. The MS-TNPI method is useful for studying a variety of extended quantum systems coupled with solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks

We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy. The input dataset consists of poloidal profiles of complex Fourier amplitudes with an n = 1 toroidal mode number from 144 human-labeled ITER Baseline Scenario discharges in the DIII-D tokamak, spanning both tearing-dominated and sawtooth-dominated regimes. Since m, n = 2,1 NTMs frequently emerge alongside sawteeth at the same frequency in this scenario, the focus is on isolating the m = 1 and m = 2 components of the n = 1 MHD mode near the tearing onset. To improve model regularization and prediction stability, singular value decomposition was applied to balance the sawtooth and tearing datasets. The enriched datasets facilitated training neural networks that learn the key distinguishing features of sawtooth and tearing modes in the poloidal profiles of their magnetic amplitude and phase. When the modes occur independently, the networks achieve perfect classification due to the modes’ distinct characteristics and low measurement noise. In the more experimentally relevant case where both modes coexist, the networks maintain exceptional performance across key metrics. Tests on synthetic data with known ground truth demonstrate the superior accuracy of the neural network trained on complex-valued input compared to models using real amplitude, phase, or pseudo-complex data, achieving both a mean time delay and standard deviation below 1 ms. Notably, standard linear regression methods fitting the dominant singular modes to the data closely match the neural network’s performance. Applying these methods across a broad range of H-mode scenarios will enable future studies to systematically identify dominant NTM triggers as scenario-specific variables, paving the way for more effective tearing mode avoidance strategies in future fusion reactor designs.

machine learning↗