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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Polymer informatics: Current status and critical next steps

Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymers. Surrogate models are trained on available polymer data for instant property prediction, allowing screening of promising polymer candidates with specific target property requirements. Questions regarding synthesizability, and potential (retro)synthesis steps to create a target polymer, are being explored using statistical means. Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored. Other major hurdles for polymer informatics are the lack of widespread availability of curated and organized data, and approaches to create machine-readable representations that capture not just the structure of complex polymeric situations but also synthesis and processing conditions. Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated. As various parts of the burgeoning polymer informatics ecosystem mature and become integrated, efficiency improvements, accelerated discoveries and increased productivity can result. Here in this paper, we review emergent components of this polymer informatics ecosystem and discuss imminent challenges and opportunities.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Investigating the domain of validity of the Gubser solution to the Boltzmann equation

Here, we study the evolution of the one particle distribution function that solves exactly the relativistic Boltzmann equation within the relaxation time approximation for a conformal system undergoing simultaneously azimuthally symmetric transverse and boost-invariant longitudinal expansion. We show, for arbitrary values of the shear viscosity to entropy density ratio, that the distribution function can become negative in certain kinematic regions of the available phase space depending on the boundary conditions. For thermal equilibrium initial conditions, we determine numerically the physical boundary in phase space where the distribution function is always positive definite. The requirement of positivity of this particular exact solution restricts its domain of validity, and it imposes physical constraints on its applicability.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A physical method for downscaling land surface temperatures using surface energy balance theory

Fine-resolution land surface temperature (LST) derived from thermal infrared remote sensing images is a good indicator of surface water status and plays an essential role in the exchange of energy and water between land and atmosphere. A physical surface energy balance (SEB)-based LST downscaling method (DTsEB) is developed to downscale coarse remotely sensed thermal infrared LST products with fine-resolution visible and near-infrared data. Here, the DTsEB method is advantageous for its ability to mechanically interrelate surface variables contributing to the spatial variation of LST, to quantitatively weigh the contributions of each related variable within a physical framework, and to efficaciously avoid the subjective selection of scaling factors and the establishment of statistical regression relationships. The applicability of the DTsEB method was tested by downscaling 12 scenes of 990 m Moderate Resolution Imaging Spectroradiometer (MODIS) and aggregated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST products to 90 m resolution at six overpass times between 2005 and 2015 over three 9.9 km by 9.9 km cropland (mixed by grass, tree, and built-up land) study areas. Three typical LST downscaling methods, namely the widely applied TsHARP, the later developed least median square regression downscaling (LMS) and the geographically weighted regression (GWR), were introduced for intercomparison. The results showed that the DTsEB method could more effectively reconstruct the subpixel spatial variations in LST within the coarse-resolution pixels and achieve a better downscaling accuracy than the TsHARP, LMS and GWR methods. The DTsEB method yielded, on average, root mean square errors (RMSEs) of 2.01 K and 1.42 K when applied to the MODIS datasets and aggregated ASTER datasets, respectively, which were lower than those obtained with the TsHARP method, with average RMSEs of 2.41 K and 1.71 K, the LMS method, with average RMSEs of 2.35 K and 1.63 K, and the GWR method, with average RMSEs of 2.38 K and 1.64 K, respectively. The contributions of the related surface variables to the subpixel spatial variation in the LST varied both spatially and temporally and were different from each other. In summary, the DTsEB method was demonstrated to outperform the TsHARP, LMS, and GWR methods and could be used as a good alternative for downscaling LST products from coarse to fine resolution with high robustness and accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Thermal Hysteresis and Ordering Behavior of Magnetic Skyrmion Lattices

The physics of phase transitions in two-dimensional (2D) systems underpins research in diverse fields including statistical mechanics, nanomagnetism, and soft condensed matter. However, many aspects of 2D phase transitions are still not well understood, including the effects of interparticle potential, polydispersity, and particle shape. Magnetic skyrmions are chiral spin-structure quasi-particles that form two-dimensional lattices. Here, in this study, we show, by real-space imaging using in situ cryo-Lorentz transmission electron microscopy coupled with machine learning image analysis, the ordering behavior of Néel skyrmion lattices in van der Waals Fe 3 GeTe 2 . We demonstrate a distinct change in the skyrmion size distribution during field-cooling, which leads to a loss of lattice order and an evolution of the skyrmion liquid phase. Remarkably, the lattice order is restored during field heating and demonstrates a thermal hysteresis. This behavior is explained by the skyrmion energy landscape and demonstrates the potential to control the lattice order in 2D phase transitions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

The cosmic DANCe of Perseus

Context. Star-forming regions are excellent benchmarks for testing and validating theories of star formation and stellar evolution. The Perseus star-forming region, being one of the youngest (< 10 Myr), closest (280-320 pc), and most studied in the literature, is a fundamental benchmark. Aims. We aim to study the membership, phase-space structure, mass, and energy (kinetic plus potential) distribution of the Perseus star-forming region using public catalogues (Gaia, APOGEE, 2MASS, and Pan-STARRS). Methods. We used Bayesian methodologies that account for extinction to identify the Perseus physical groups in the phase-space, retrieve their candidate members, derive their properties (age, mass, 3D positions, 3D velocities, and energy), and attempt to reconstruct their origin. Results. We identify 1052 candidate members in seven physical groups (one of them new) with ages between 3 and 10 Myr, dynamical super-virial states, and large fractions of energetically unbounded stars. Their mass distributions are broadly compatible with that of Chabrier for masses ≳0.1 M ⊙ and do not show hints of over-abundance of low-mass stars in NGC 1333 with respect to IC 348. These groups’ ages, spatial structure, and kinematics are compatible with at least three generations of stars. Future work is still needed to clarify if the formation of the youngest was triggered by the oldest. Conclusions. The exquisite Gaia data complemented with public archives and mined with comprehensive Bayesian methodologies allow us to identify 31% more members than previous studies, discover a new physical group (Gorgophone: 7 Myr, 191 members, and 145 M ⊙ ), and confirm that the spatial, kinematic, and energy distributions of these groups support the hierarchical star formation scenario.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Comparison of fluid neutral models for one-dimensional plasma edge modeling with a finite volume solution of the Boltzmann equation

We derive fluid neutral approximations for a simplified 1D edge plasma model, suitable to study the neutral behavior close to the target of a nuclear fusion divertor, and compare its solutions to the solution of the corresponding kinetic Boltzmann equation. The plasma is considered as a fixed background extracted from a detached 2D simulation. We show that the Maxwellian equilibrium distribution is already obtained very close to the target, justifying the use of a fluid approximation. We compare three fluid neutral models: (i) a diffusion model; (ii) a pressure-diffusion model (i.e., a combination of a continuity and momentum equation) assuming equal neutral and ion temperatures; and (iii) the pressure-diffusion model coupled to a neutral energy equation taking into account temperature differences between neutrals and ions. Partial reflection of neutrals reaching the boundaries is included in both the kinetic and fluid models. We propose two methods to obtain an incident neutral flux boundary condition for the fluid models: one based on a diffusion approximation and the other assuming a truncated Chapman-Enskog distribution. The pressure-diffusion model predicts the plasma sources very well. The diffusion boundary condition gives slightly better results overall. Although including an energy equation still improves the results, the assumption of equal ion and neutral temperature already gives a very good approximation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Dynamics of a reconnection-driven runaway ion tail in a reversed field pinch plasma

While reconnection-driven ion heating is common in laboratory and astrophysical plasmas, the underlying mechanisms for converting magnetic to kinetic energy remain not fully understood. Reversed field pinch discharges are often characterized by rapid ion heating during impulsive reconnection, generating an ion distribution with an enhanced bulk temperature, mainly perpendicular to magnetic field. In the Madison Symmetric Torus, a subset of discharges with the strongest reconnection events develop a very anisotropic, high energy tail parallel to magnetic field in addition to bulk perpendicular heating, which produces a fusion neutron flux orders of magnitude higher than that expected from a Maxwellian distribution. Here, we demonstrate that two factors in addition to a perpendicular bulk heating mechanism must be considered to explain this distribution. First, ion runaway can occur in the strong parallel-to-B electric field induced by a rapid equilibrium change triggered by reconnection-based relaxation; this effect is particularly strong on perpendicularly heated ions which experience a reduced frictional drag relative to bulk ions. Second, the confinement of ions varies dramatically as a function of velocity. Whereas thermal ions are governed by stochastic diffusion along tearing-altered field lines (and radial diffusion increases with parallel speed), sufficiently energetic ions are well confined, only weakly affected by a stochastic magnetic field. High energy ions traveling mainly in the direction of toroidal plasma current are nearly classically confined, while counter-propagating ions experience an intermediate confinement, greater than that of thermal ions but significantly less than classical expectations. The details of ion confinement tend to reinforce the asymmetric drive of the parallel electric field, resulting in a very asymmetric, anisotropic distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Theoretical investigation of carrier transfer by an optical contacting scheme for optoelectronic application

As a promising charge carrier transfer scheme, optical coupling could potentially improve the performance of an optoelectronic device for energy harvesting based on well developed nanotechnology. By extracting carriers optically, the functional features of the nano-structured material could be better used by minimizing the concerns about its electrical properties. In this paper, we present a rigorous electromagnetic model to analyze the optical carrier transfer problem. The flow of the energy is analyzed carefully by the photon transfer spectrum, and the photon emitters (electron-hole pairs) are assumed in a thermal equilibrium described by Bose-Einstein distribution. The result shows that an energy selective carrier transfer can be optically achieved at the device level by integrating the emitter and receiver into a nano-optical resonator, where both the photon emission and absorption are significantly amplified by a near-field coupling around the resonant frequency. General design and optimization schemes in practice are addressed by examining the influence of the photonic design and an energy dependent emissivity of the emitter, which can be used to develop the optical contacting concept further.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Nonlinear proper orthogonal decomposition for convection-dominated flows

Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space. This reduced order representation offers a modular data-driven modeling approach for nonlinear dynamical systems when integrated with a time series predictive model. In this Letter, we put forth a nonlinear proper orthogonal decomposition (POD) framework, which is an end-to-end Galerkin-free model combining autoencoders with long short-term memory networks for dynamics. By eliminating the projection error due to the truncation of Galerkin models, a key enabler of the proposed nonintrusive approach is the kinematic construction of a nonlinear mapping between the full-rank expansion of the POD coefficients and the latent space where the dynamics evolve. We test our framework for model reduction of a convection-dominated system, which is generally challenging for reduced order models. Our approach not only improves the accuracy, but also significantly reduces the computational cost of training and testing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Improved loss functions for machine-learned atomic potentials

Machine learning (ML) has become an invaluable tool across a wide array of domains in science as researchers find new ways to leverage its predictive power. This is especially true in chemistry, where ML is used to fit chemical properties or desirable attributes to the local structure of molecules and materials. In the pursuit of greater accuracy, it is relatively simple to increase the size or complexity of such models, although this often requires simultaneously seeking larger datasets in order to both fit and interpret the larger number of parameters. However, it is equally important to assess the quality and relative importance of the data and how these factors impact the training process. We, therefore, investigate the impact of using different loss functions for training neural network potentials (NNPs), as the loss function defines the error and parameter gradients used to train the NNP. In particular, we test the mean-squared error and Huber loss functions and, using insight from these functions, derive a new loss function based on the Asinh function, which yields significant improvement in the accuracy and generality of NNPs. We show that by discounting/minimizing errors and anomalies in the optimization process, both the Huber and Asinh loss functions improve the training of NNPs, leading to a final potential with a greater effective dimensionality.

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

MULTIPLICITY AMONG F-TYPE STARS. II

In continuation of our previous study we present an updated census of new companions and model atmosphere analyses for some 50 southern dwarfs, mostly in the mass range 0.90⩽M⩽1.10 M{sub ⊙}. For the common-proper-motion companions μ Vir B, HR 2225 B, HD 67199 B, and HD 114853 B, we confirm their physical association from their radial velocities. We report the discovery of the F-type visual binary α For as a field blue straggler and confirm (ζ Ret, HR 5864) or identify (HD 67199, HR 4013, HR 8843) another five mass transfer systems or candidates. For the F stars τ{sup 1} Eri and 111 Tau, we present 10σ and 7σ cases for astrometric binaries by virtue of the very accurate van Leeuwen Hipparcos parallaxes. Following the work of Shaya and Olling, we suggest the F-type star ι Vir to be a wide (0.37 pc) hierarchical quadruple system. We confirm the visual binary NLTT 23781/2 as a common-proper-motion object to the very wide (0.54 pc) F star 40 Leo, but discard the G star HD 128987 as an ultra-wide (1.01 pc) physical companion to the α Lib quadruple system on account of a diverse metallicity. The improved statistics of our sample establishes the previously discovered positive correlation of stellar multiplicities with primary mass. For the F star multiplicity census in the mass range 1.10⩽M⩽1.70 M{sub ⊙}, we find that at least a quarter consists of triple or higher level systems and at least two out of three F stars are non-single.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Warm dark matter via ultra-violet freeze-in: reheating temperature and non-thermal distribution for fermionic Higgs portal dark matter

Warm dark matter (WDM) of order keV mass may be able to resolve the disagreement between structure formation in cold dark matter simulations and observations. The detailed properties of WDM will depend upon its energy distribution, in particular how it deviates from the thermal distribution usually assumed in WDM simulations. Here we focus on WDM production via the Ultra-Violet (UV) freeze-in mechanism, for the case of fermionic Higgs portal dark matter ψ produced via the portal interaction ψ-barψH{sup †}H/Λ. We introduce a new method to simplify the computation of the non-thermal energy distribution of dark matter from freeze-in. We show that the non-thermal energy distribution from UV freeze-in is hotter than the corresponding thermal distribution and has the form of a Bose-Einstein distribution with a non-thermal normalization. The resulting range of dark matter fermion mass consistent with observations is 5–7 keV. The reheating temperature must satisfy T{sub R}≳120 GeV in order to account for the observed dark matter density when m{sub ψ}≈5 keV, where the lower bound on T{sub R} corresponds to the limit where the fermion mass is entirely due to electroweak symmetry breaking via the portal interaction. The corresponding bound on the interaction scale is Λ≳6.0×10{sup 9} GeV.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Background rejection in NEXT using deep neural networks

Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

The use of minimal spanning trees in particle physics

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Machine learning techniques in searches for$t\bar{t}$h in the h → $b\bar{b}$ decay channel

Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely large mass of the top quark plays a special role in electroweak symmetry breaking. Higgs bosons decay predominantly to b$\bar{_b}$, yielding signatures for the signal that are similar to t$\bar{_t}$ + jets with heavy flavor. Though particularly challenging to study due to the similar kinematics between signal and background events, such final states (t$\bar{_t}$b$\bar{b}$) are an important channel for studying the top quark Yukawa coupling. This paper presents a systematic study of machine learning (ML) methods for detecting t$\bar{_t}$h in the h → b$\bar{b}$ decay channel. Among the seven ML methods tested, we show that neural network models outperform alternative methods. In addition, two neural models used in this paper outperform NeuroBayes, one of the standard algorithms used in current particle physics experiments. We further study the effectiveness of ML algorithms by investigating the impact of feature set and data size, as well as the depth of the networks for neural models. We demonstrate that an extended feature set leads to improvement of performance over basic features. Furthermore, the availability of large samples for training is found to be important for improving the performance of the techniques. For the features and the data set studied here, neural networks of more layers deliver comparable performance to their simpler counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Data Unfolding with Wiener-SVD Method

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

3D modeling of electric fields in the LUX detector

This work details the development of a three-dimensional (3D) electric field model for the LUX detector. The detector took data to search for weakly interacting massive particles (WIMPs) during two periods. After the first period completed, a time-varying non-uniform negative charge developed in the polytetrafluoroethylene (PTFE) panels that define the radial boundary of the detector's active volume. This caused electric field variations in the detector in time, depth and azimuth, generating an electrostatic radially-inward force on electrons on their way upward to the liquid surface. To map this behavior, 3D electric field maps of the detector's active volume were generated on a monthly basis. This was done by fitting a model built in COMSOL Multiphysics to the uniformly distributed calibration data that were collected on a regular basis. The modeled average PTFE charge density increased over the course of the exposure from -3.6 to -5.5 μC/m 2 . Here, from our studies, we deduce that the electric field magnitude varied locally while the mean value of the field of ~200 V/cm remained constant throughout the exposure. As a result of this work the varying electric fields and their impact on event reconstruction and discrimination were successfully modeled.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Publishing unbinned differential cross section results

Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinned measurements can enable, improve, or at least simplify comparisons between experiments and with theoretical predictions. Furthermore, many-dimensional measurements can be used to define observables after the measurement instead of before. There is currently no community standard for publishing unbinned data. While there are also essentially no measurements of this type public, unbinned measurements are expected in the near future given recent methodological advances. The purpose of this paper is to propose a scheme for presenting and using unbinned results, which can hopefully form the basis for a community standard to allow for integration into analysis workflows. This is foreseen to be the start of an evolving community dialogue, in order to accommodate future developments in this field that is rapidly evolving.

47 OTHER INSTRUMENTATION↗