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DOE OSTI2025

Genetic Transfer in Action: Uncovering DNA Flow in an Extremophilic Microbial Community

ABSTRACT Horizontal genetic transfer (HGT) is a significant driver of genomic novelty in all domains of life. HGT has been investigated in many studies however, the focus has been on conspicuous protein‐coding DNA transfers that often prove to be adaptive in recipient organisms and are therefore fixed longer‐term in lineages. These results comprise a subclass of HGTs and do not represent exhaustive (coding and non‐coding) DNA transfer and its impact on ecology. Uncovering exhaustive HGT can provide key insights into the connectivity of genomes in communities and how these transfers may occur. In this study, we use the term frequency‐inverse document frequency (TF‐IDF) technique, that has been used successfully to mine DNA transfers within real and simulated high‐quality prokaryote genomes, to search for exhaustive HGTs within an extremophilic microbial community. We establish a pipeline for validating transfers identified using this approach. We find that most DNA transfers are within‐domain and involve non‐coding DNA. A relatively high proportion of the predicted protein‐coding HGTs appear to encode transposase activity, restriction‐modification system components, and biofilm formation functions. Our study demonstrates the utility of the TF‐IDF approach for HGT detection and provides insights into the mechanisms of recent DNA transfer.

Microbiology↗

DOE OSTI2019

HEMP Transformer Defense Through Power Electronics

High altitude electromagnetic pulse (HEMP) generated by exoatmospheric nuclear burst has the ability to affect the electric power grid. The main power grid components, cables and transformers, act as large antennas that have current induced in them from the HEMP. These currents are called geomagneticallyinduced currents (GIC), and they can occur during geomagnetic disturbances from HEMP or solar flares. HEMPs produce pulses with high electric field strengths that breakdown the dielectric of most electronics and, it is broken into three stages: E1 pulse with a microsecond width, E2 the falling trail of the pulse, and E3 a constant DC component. Oak Ridge National Laboratory has proven that transformers fitted with lightning arrestors do not receive damage from E1 and E2 pulses. However, the DC component of the E3 causes the transformer’s core to saturate and induce rapid heating that damages the transformer. SRNL is developing a power electronic system that is connected to the neutral of the high voltage side of large power transformers (LPT). This system will inject the inverse of the DC effect from the HEMP into the primary side of the transformer canceling out the E3 waveform and protecting the transformer from half-cycle-saturation. This year SRNL successfully simulated removing the DC injected and accurately simulated saturation on the transformer.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

NuSTAR Perspective on High-redshift MeV Blazars

With bolometric luminosities exceeding 10 48 erg s -1 , powerful jets, and supermassive black holes at their center, MeV blazars are some of the most extreme sources in the universe. Recently, the Fermi-Large Area Telescope detected five new γ-ray emitting MeV blazars beyond redshift z = 3.1. With the goal of precisely characterizing the jet properties of these extreme sources, we started a multiwavelength campaign to follow them up with joint Nuclear Spectroscopic Telescope Array, Swift, and the Southeastern Association for Research in Astronomy’s optical telescopes. We observe six high-redshift quasars, four of them belonging to the new γ-ray emitting MeV blazars. Thorough X-ray analysis reveals spectral flattening at soft X-ray for three of these objects. The source NVSS J151002+570243 also shows a peculiar rehardening of the X-ray spectrum at energies E > 6 keV. Adopting a one-zone leptonic emission model, this combination of hard X-rays and γ-rays enables us to determine the location of the Inverse Compton peak and to accurately constrain the jet characteristics. In the context of the jet-accretion disk connection, we find that all six sources have jet powers exceeding accretion disk luminosity, seemingly validating this positive correlation even beyond z > 3. Our six sources are found to have 10 9 M ⊙ black holes, further raising the space density of supermassive black holes in the redshift bin z = [3, 4].

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Human-automated vehicle interactions

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

DOE OSTI2021

HUMAN-AUTOMATED VEHICLE INTERACTIONS

Guo, Longxiang↗

DOE OSTI2024

Parametric description of intermittent probability distribution functions in solar wind and magnetohydrodynamic turbulence

ABSTRACT In this work, we find empirical evidence that the scale-dependent statistical properties of solar wind and magnetohydrodynamic (MHD) turbulence can be described in terms of a family of parametric probability distribution functions (PDFs) known as Normal Inverse Gaussian (NIG). Understanding these PDFs is one of the most important goals in turbulence theory, as they are inherently connected to the intermittent properties of solar wind turbulence. We investigate the properties of PDFs of Elsasser increments based on a large statistical sample from solar wind observations and high-resolution numerical simulations of MHD turbulence. In order to measure the PDFs and their corresponding properties, three experiments are presented: fast and slow solar wind for experimental data and a simulation of reduced MHD (RMHD) turbulence. Conditional statistics on a 23-yr-long sample of WIND data near 1 au and high-resolution pseudo-spectral simulation of steadily driven RMHD turbulence on a $2048^3$ mesh are used to construct scale-dependent PDFs. The empirical PDFs are fitted to NIG distributions, which depend on four free parameters. Our analysis shows that NIG distributions accurately capture the evolution of the PDFs, with scale-dependent parameters, from large scales characterized by a Gaussian distribution, turning to exponential tails within the inertial range and stretched exponentials at dissipative scales. We also show that empirically-measured NIG parameters exhibit well-defined scaling properties that are similar across the three empirical data sets, which may be indicative of universal behaviour.

Astronomy & Astrophysics↗

DOE OSTI2021

Progressive transfer learning for low-frequency data prediction in full waveform inversion

To effectively overcome the cycle-skipping issue in full waveform inversion (FWI), we developed a deep neural network (DNN) approach to predict the absent low-frequency components by exploiting the hidden physical relation connecting the low- and the high-frequency data. To efficiently solve this challenging nonlinear regression problem, two novel strategies were proposed to design the DNN architecture and to optimize the learning process: (1) dual data feed structure; (2) progressive transfer learning. With the dual data feed structure, not only the high-frequency data, but also the corresponding beat tone data are fed into the DNN to relieve the burden of feature extraction. The second strategy, progressive transfer learning, enables us to train the DNN using a single evolving training dataset. Within the framework of the progressive transfer learning, the training dataset continuously evolves in an iterative manner by gradually retrieving the subsurface information through the physics-based inversion module, progressively enhancing the prediction accuracy of the DNN and propelling the inversion process out of the local minima. Here, the synthetic numerical experiments suggest that, without any a priori geological information, the low-frequency data predicted by the progressive transfer learning are sufficiently accurate for an FWI engine to produce reliable subsurface velocity models free of cycle-skipping artifacts.

02 PETROLEUM↗

DOE OSTI2025

Convergence Analysis of the Alternating Anderson–Picard Method for Nonlinear Fixed-Point Problems

Anderson acceleration (AA) has been widely used to solve nonlinear fixed-point problems due to its rapid convergence. This work focuses on a variant of AA in which multiple Picard iterations are performed between each AA step, referred to as the Alternating Anderson–Picard (AAP) method. Furthermore, despite introducing more “slow” Picard iterations, this method has been shown to be efficient and even more robust in both linear and nonlinear cases. However, there is a lack of theoretical analysis for AAP in the nonlinear case. In this paper, we address this gap by establishing the equivalence between AAP and a multisecant-GMRES method that uses GMRES to solve a multisecant linear system at each iteration. From this perspective, we show that AAP “converges” to the Newton-GMRES method. Specifically, as the residual approaches zero, the multisecant matrix, the approximate Jacobian inverse, the search direction, and the optimization gain of AAP converge to their counterparts in the Newton-GMRES method. These connections provide insights for analyzing the asymptotic convergence properties of AAP. Consequently, we show that AAP is locally 𝑞-linear convergent and provide an upper bound for the convergence factor of AAP. To validate the theoretical results, numerical examples are provided.

Anderson acceleration↗

DOE OSTI2021

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks

The Fokker--Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines, but it requires specification of the coefficients for each case, which can be functions of space-time and not just constants and hence require the development of a data-driven modeling approach. When the data available is directly on the PDF, there exist methods for inverse problems that can be employed to infer the coefficients and thus determine the FP equation and subsequently obtain its solution. Herein, we address a more realistic scenario, where only sparse data are given on the particles' positions at a few time instants, which are not sufficient to accurately construct directly the PDF even at those times from existing methods, e.g., kernel estimation algorithms. To this end, we develop a general framework based on physics-informed neural networks (PINNs) that introduces a new loss function using the Kullback--Leibler divergence to connect the stochastic samples with the FP equation to simultaneously learn the equation and infer the multidimensional PDF at all times. In particular, we consider two types of inverse problems, type I, where the FP equation is known but the initial PDF is unknown, and type II, in which, in addition to the unknown initial PDF, the drift and diffusion terms are also unknown. In both cases, we investigate problems with either Brownian or Lévy noise or a combination of both. Here, we demonstrate the new PINN framework in detail in the one-dimensional (1D) case, but we also provide results for up to five dimensions demonstrating that we can infer both the FP equation and dynamics simultaneously at all times with high accuracy using only very few discrete observations of the particles.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Emergence of Fermi arcs due to magnetic splitting in an antiferromagnet

The Fermi surface plays an important role in controlling the electronic, transport and thermodynamic properties of materials. As the Fermi surface consists of closed contours in the momentum space for well-defined energy bands, disconnected sections known as Fermi arcs can be signatures of unusual electronic states, such as a pseudogap. Another way to obtain Fermi arcs is to break either the time-reversal symmetry or the inversion symmetry of a three-dimensional Dirac semimetal, which results in formation of pairs of Weyl nodes that have opposite chirality, and their projections are connected by Fermi arcs at the bulk boundary. Here, we present experimental evidence that pairs of hole- and electron-like Fermi arcs emerge below the Neel temperature (TN) in the antiferromagnetic state of cubic NdBi due to a new magnetic splitting effect. The observed magnetic splitting is unusual, as it creates bands of opposing curvature, which change with temperature and follow the antiferromagnetic order parameter. This is different from previous theoretically considered and experimentally reported cases of magnetic splitting, such as traditional Zeeman and Rashba, in which the curvature of the bands is preserved. Therefore, our findings demonstrate a type of magnetic band splitting in the presence of a long-range antiferromagnetic order that is not readily explained by existing theoretical ideas.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Symmetry instability induced by topological phase transitions

The symmetry-topology interplay dictates how to define order parameters and classify material ordered phases. However, current understanding of this interplay has been predominately approached from a one-sided perspective, with topological states being classified within the constraints imposed by specific fixed symmetries. Here, in this study, we complete this full circle by demonstrating spontaneous symmetry breaking that results from a periodic alteration of topological phases induced by light in a centrosymmetric Dirac material ZrTe 5 . The distinguishing feature is the observation of robust correlation and striking anomalies in the fluence and temperature dependence of key transport parameters. First, both shift current 𝐽 𝑠 and displacement current 𝐽 𝑑 , arising from interband transition and infrared phonon driving, respectively, along with charge carrier pumping, exhibit similar behaviors. Second, they all peak at similar low pump fluence, followed by a subsequent reduction as the fluence further increases. This behavior cannot be explained by conventional energetically allowed, direct excitations. Third, all the three observables exhibit anomalies when they approach the topological phase transition temperature. These results highlight the unique low-energy pumping behaviors in ZrTe 5 , characterized by reversible fluence dependence and a “hinge-like” interaction that connects various electronic and lattice observables, including phonons, charge carriers, and currents. Our findings, supported by model analysis, provide key insights into the fragility of crystalline (inversion) and time-reversal symmetries during the dynamics of topological phase transitions. This fragility drives spontaneous symmetry breaking, evidenced by the synchronized emergence of off-resonant infrared phonons and broken-symmetry photocurrents.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Nanoribbons or weakly connected acenes? The influence of pyrene insertion on linearly extended ring systems

Derived from the lateral fusion of benzene rings, acenes are a class of π-conjugated molecules containing a single aromatic sextet, where system size is inversely correlated with chemical stability. In the pursuit of creating graphene nanoribbons/nanowires, several extended-ring structures have been synthesized through linear combinations of azaacenes and pyrene. Importantly, these extended systems demonstrate enhanced chemical stability and allow for the construction of macromolecular-sized structures. In this report we present a combined quantum-chemical and experimental study to reveal the cost of these improved characteristics in fully carbon-based systems. The results clearly show that pyrene moieties inserted among acene units do not result in long acene-like structures, rather the pyrene-inserted acene is, electronically, a series of (nearly) isolated acenes. The origin of pyrene's electronic blocking effect and implications on oxidized and photoexcited states of these extended-ring systems are detailed. The results of this investigation definitively show that coupling pyrene in an orthogonal orientation (through the 4, 5/9, 10 positions or e/l faces) to acenes should be eschewed if nanographene-/nanowire-like structures are desired.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Structural and thermal properties of Na 2 Mn(SO 4 ) 2 ·4H 2 O and Na 2 Ni(SO 4 ) 2 ·10H 2 O

The title compounds were prepared via a wet chemistry route and their crystal structures were determined from single crystal X-ray diffraction data. Na 2 Mn(SO 4 ) 2 ·4H 2 O crystallizes with a monoclinic symmetry, space group P2 1 /c, with a = 5.5415(2), b = 8.3447(3), c = 11.2281(3) Å, β = 100.172(1)°, V = 511.05(3) Å 3 and Z = 2. Na 2 Ni(SO 4 ) 2 ·10H 2 O also crystallizes with a monoclinic symmetry, space group P2 1 /c, with a = 12.5050(8), b = 6.4812(4), c = 10.0210(6) Å, β = 106.138(2)°, V = 780.17(8) Å3 and Z = 2. Na 2 Mn(SO 4 ) 2 ·4H 2 O is a new member of the blödite family of compounds, whereas Na 2 Ni(SO 4 ) 2 ·10H 2 O is isostructural with Na 2 Mg(SO 4 ) 2 ·10H 2 O. The structure of Na 2 Mn(SO 4 ) 2 ·4H 2 O is built up of [Mn(SO 4 ) 2 (H 2 O) 4 ] 2– building blocks connected through moderate O–H…O hydrogen bonds with the sodium atoms occupying the large tunnels along the a axis and the manganese atom lying on an inversion center, whereas the structure of Na 2 Ni(SO 4 ) 2 ·10H 2 O is built up of [Ni(H 2 O) 6 ] 2+ and [Na 2 (SO 4 ) 2 (H 2 O) 4 ] 2– layers. These layers which are parallel to the (100) plane are interconnected through moderate O–H…O hydrogen bonds. Here, the thermal gravimetric- and the powder X-ray diffraction-analyzes showed that only the nickel phase was almost pure. At a temperature above 300 °C, all the water molecules evaporated and a structural phase transition from P2 1 /c-Na 2 Ni(SO 4 ) 2 ·10H 2 O to C2/c-Na 2 Ni(SO 4 ) 2 was observed. C2/c-Na 2 Ni(SO 4 ) 2 is thermally more stable than Na 2 Fe(SO 4 ) 2 and therefore it would be suitable as the positive electrode for sodium ion batteries if a stable electrolyte at high voltage is developed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Expansive open Fermi arcs and connectivity changes induced by infrared phonons in ZrTe 5

Expansive open Fermi arcs covering most of the surface Brillouin zone (SBZ) are desirable for detection and control of many topological phenomena, but they have generally been reported for Kramers-Weyl points, or unconventional chiral fermions, pinned at time-reversal invariant momentum in chiral materials. Here using first-principles band structure calculations, we show that for conventional Weyl points in ZrTe 5 with the chirality of +1/–1 near the BZ center at general momentum induced by one of the infrared phonons—the second lowest $B_{1u}$ mode for breaking inversion symmetry—they can also form expansive open Fermi arcs across the SBZ boundary to occupy most of the SBZ when projected on the (001) surface. In this work, we reveal that such expansive open Fermi arcs are evolved from the topological surface states that connect multiple surface Dirac points on the (001) surface of the topological insulator phases without lattice distortion in ZrTe 5 . Furthermore, we find that the connectivity of the induced open Fermi arcs can be changed by the magnitude of the lattice distortion of this infrared phonon mode. Thus, we propose that using coherent optical phonons to modulate lattice parameters can offer ways to induce unique topological features including expansive open Fermi arcs and to dynamically control Fermi arc connectivity in ZrTe 5 .

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine learning inversion from small-angle scattering for charged polymers

We develop Monte Carlo simulations for uniformly charged polymers and a machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments. The polymer is modeled as a chain of fixed-length bonds, where the connected bonds are subject to bending energy, and there is also a screened Coulomb potential for charge interaction between all joints. The bending energy is determined by the intrinsic bending stiffness, and the charge interaction depends on the interaction strength and screening length. All three contribute to the stiffness of the polymer chain and lead to longer and larger polymer conformations. The screening length also introduces a second length scale for the polymer besides the bending persistence length. To obtain the inverse mapping from the structure factor to these polymer conformation and energy-related parameters, we generate a large data set of structure factors by running simulations for a wide range of polymer energy parameters. We use principal component analysis to investigate the intra-polymer structure factors and determine the feasibility of the inversion using the nearest neighbor distance. We employ Gaussian process regression to achieve the inverse mapping and extract the characteristic parameters of polymers from the structure factor with low relative error.

36 MATERIALS SCIENCE↗

DOE OSTI2026

FIRM: federated image reconstruction using multimodal tomographic data

Here, we propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal approaches that are prone to noise and reduced image quality, as well as the limitations of centralized multimodal approaches that require extensive data transfer, leading to significant communication overhead, storage demands, and potential data privacy concerns. Our approach formulates a joint inverse optimization problem incorporating multimodality constraints and solves it in a federated framework through local gradient computations complemented by lightweight central operations, thereby ensuring data decentralization. Leveraging the connection between our federated algorithm and the quadratic penalty method, we introduce an adaptive step-size rule with guaranteed sublinear convergence. Numerical results demonstrate superior computational efficiency and improved image reconstruction quality compared to existing approaches.

federated algorithm↗

DOE OSTI2024

Microscopic Theory of Nonlinear Hall Effect in Three-Dimensional Magnetic Systems

Abstract Nonlinear Hall effect (NLHE) has been detected in various of condensed matter systems. Unlike linear Hall effect, NLHE may exist in physical systems with broken inversion symmetry in crystals. On the other hand, real space spin texture may also break inversion symmetry and result in NLHE. We employ the Feynman diagrammatic technique to calculate non-linear Hall conductivity (NLHC) in three-dimensional magnetic systems. The results connect NLHE with the physical quantity of emergent electrodynamics which originates from the magnetic texture. The leading order contribution of NLHC, χabb , is proportional to the emergent toroidal moment T a e , which reflects how the spin textures wind in three dimensions.

Hou 侯, Wen-Tao 文涛↗