Accelerating the DC algorithm for smooth functions
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SEARCH · Engineering Papers
Results for “Computer science, Physics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Electrochemical grand potential-based phase-field simulation of electric field-assisted sintering
Here, an electrochemical grand potential functional was proposed to describe the sintering of an ionic ceramic green body. The resultant phase-field description enables simulation of the consolidation of an arbitrary number of granular particles and their interactions with the surrounding void phase. The model includes the effects of charged vacancies and the associated interactions between internal and applied electric fields. Defect segregation to grain boundaries is also accounted for, as well as enhanced interfacial defect mobilities. The model was parameterized for Y 2 O 3 . Simulations of two-particle systems showed that the applied electric field had an increasingly important impact on neck growth as particle size increased. A sudden rapid increase in temperature occurred for larger field strengths, which has been reported to be correlated to the onset of a flash event in flash sintering. Simulations of many particles showed that internal heat generation by Joule heating was localized at particle–particle contacts (grain boundaries), even though their conductivities were lower than nearby internal particle-void interfaces. A percolative path for ionic charge across the green body and the ceramic sintered solid was thus defined, accelerating the Joule heating process as the porosity of the green body is removed.
36 MATERIALS SCIENCE↗
Variationally consistent Maxwell stress in flexoelectric structures under finite deformation and immersed in free space
Maxwell stress refers to the mechanical stress exerted on a dielectric material due to the presence of electric fields. It plays a significant role in the interaction between a dielectric material and the surrounding free space under finite deformation. Previous research on finite deformation of flexoelectricity mainly adopted a modified form of Maxwell stress, potentially not able to correctly capture some physical phenomena, such as the compression of a dielectric droplet in an electric field. In this work, we propose a consistent and complete variational principle for flexoelectricity, in which the Maxwell stress emerges naturally from the derivation, without introducing additional assumptions. An Isogeometric analysis-based numerical framework is developed accordingly and verified by both linear and nonlinear benchmark cases compared with experimental results. The present framework successfully captures and quantifies the behaviors of conductive liquids and soft dielectric solids subjected to an external electric field. Finally, a novel scenario is investigated in which a flexoelectric beam immersed in free space is analyzed, showing the interesting distribution of Maxwell stress-induced tractions at opposing boundaries. The test demonstrates that a higher dielectric constant can effectively enhance the material's stiffness in response to the external electric loading.
36 MATERIALS SCIENCE↗
Theory-Guided Machine Learning Finds Geometric Structure-Property Relationships for Chemisorption on Subsurface Alloys
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Towards physics-informed explainable machine learning and causal models for materials research
From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.
36 MATERIALS SCIENCE↗
Fission Reaction Event Yield Algorithm FREYA 2.0.2
The purpose of this paper is to present the main differences between FREYA versions 1.0 and 2.0.2. FREYA (Fission Reaction Event Yield Algorithm) is a fission event generator which models complete fission events. As such, it automatically includes fluctuations as well as correlations between observables, resulting from conservation of energy and momentum. The main differences between the two versions are: additional fissionable isotopes, angular momentum conservation, Giant Dipole Resonance form factor for the statistical emission of photons, improved treatment of fission photon emission using RIPL database, and dependence on the incident neutron direction. FREYA 2.0.2 has been integrated into the LLNL Fission Library 2.0.2, which has itself been integrated into MCNP6.2, TRIPOLI-4.10, and can be called from Geant4.10.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
TTDFT: A GPU accelerated Tucker tensor DFT code for large-scale Kohn-Sham DFT calculations
We present the Tucker tensor DFT (TTDFT) code which uses a tensor-structured algorithm with graphic processing unit (GPU) acceleration for conducting ground-state DFT calculations on large-scale systems. The Tucker tensor DFT algorithm uses a localized Tucker tensor basis computed from an additive separable approximation to the Kohn-Sham Hamiltonian. The discrete Kohn-Sham problem is solved using Chebyshev filtered subspace iteration method that relies on matrix-matrix multiplications of a sparse symmetric Hamiltonian matrix and a dense wavefunction matrix, expressed in the localized Tucker tensor basis. These matrix-matrix multiplication operations, which constitute the most computationally intensive step of the solution procedure, are GPU accelerated providing ~8-fold GPU-CPU speedup for these operations on the largest systems studied. In conclusion, the computational performance of the TTDFT code is presented using benchmark studies on aluminum nano-particles and silicon quantum dots with system sizes ranging up to ~7,000 atoms.
97 MATHEMATICS AND COMPUTING↗
MSTor 2023 : A new version of the computer code for multistructural torsional anharmonicity, now with automatic torsional identification using redundant internal coordinates
The MSTor program is a computer program for calculating partition functions and thermodynamic functions of complex gas-phase molecules with multiple torsions; the multi-structural approximation with torsional anharmonicity (MS-T) can be used based on either a coupled torsional potential or an uncoupled torsional potential. The program can also carry out calculations in the multiple-structure local harmonic (MS-LH) approximation or multi-structural local quasiharmonic (MS-LQ) approximation or by the dual-level MS-T method. Furthermore, the program package includes eight utility codes that can be used as stand-alone programs to help the user to generate the input files for the code or to generate comparison results. The 2023 version of MSTor includes a new capability, namely automatic identification of torsional modes with redundant internal coordinates.
97 MATHEMATICS AND COMPUTING↗
Introduction to MDSplus using Docker
With increased use of MDSplus comes an influx of new users. With them comes a need for more and better ways to learn the suite of tools that is MDSplus. Therefore, the MDSplus team continually evaluates new technologies to improve our software and user experience. To this end, we investigated Docker to determine if and how it could help new users understand MDSplus, make MDSplus easier to install, and allow us to easily test old/new versions. To achieve this, a set of Docker images and instructions have been developed. This paper will provide an overview of MDSplus, and detail the methods to create and use the Docker images. Additionally, we will explore the limitations of such an approach, and the recommended applications. The project where these Docker Images were built, along with the Demo is here: https://github.com/WhoBrokeTheBuild/DockerizedMDSplus https://hub.docker.com/r/whobrokethebuild/mdsplus The now official Docker Images are available here: https://github.com/MDSplus/Docker https://hub.docker.com/r/mdsplus/mdsplus
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations
Data-driven discovery of differential equations has been an emerging research topic. We propose a novel algorithm subsampling-based threshold sparse Bayesian regression (SubTSBR) to tackle high noise and outliers. The subsampling technique is used for improving the accuracy of the Bayesian learning algorithm. It has two parameters: subsampling size and the number of subsamples. When the subsampling size increases with fixed total sample size, the accuracy of our algorithm goes up and then down. When the number of subsamples increases, the accuracy of our algorithm keeps going up. We demonstrate how to use our algorithm step by step and compare our algorithm with threshold sparse Bayesian regression (TSBR) for the discovery of differential equations. We show that our algorithm produces better results. We also discuss the merits of discovering differential equations from data and demonstrate how to discover models with random initial and boundary condition as well as models with bifurcations. The numerical examples are: (1) predator-prey model with noise, (2) shallow water equations with outliers, (3) heat diffusion with random initial and boundary condition, and (4) fish-harvesting problem with bifurcations.
97 MATHEMATICS AND COMPUTING↗
A low-rank power iteration scheme for neutron transport criticality problems
Computing effective eigenvalues for neutron transport often requires a fine numerical resolution. Here, the main challenge of such computations is the high memory effort of classical solvers, which limits the accuracy of chosen discretizations. In this work, we derive a method for the computation of effective eigenvalues when the underlying solution has a low-rank structure. This is accomplished by utilizing dynamical low-rank approximation (DLRA), which is an efficient strategy to derive time evolution equations for low-rank solution representations. The main idea is to interpret the iterates of the classical inverse power iteration as pseudo-time steps and apply the DLRA concepts in this framework. In our numerical experiment, we demonstrate that our method significantly reduces memory requirements while achieving the desired accuracy. Analytic investigations show that the proposed iteration scheme inherits the convergence speed of the inverse power iteration, at least for a simplified setting.
97 MATHEMATICS AND COMPUTING↗
B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD
Here, the Deep Operator Network (DeepONet) is a neural network architecture used to approximate operators, including the solution operator of parametric PDEs. DeepONets have shown remarkable approximation ability. However, the performance of DeepONets deteriorates when the training data is polluted with noise, a scenario that occurs in practice. To handle noisy data, we propose a Bayesian DeepONet based on replica exchange Langevin diffusion (reLD). Replica exchange uses two particles. The first particle trains a DeepONet to exploit the loss landscape and make predictions. The other particle trains a different DeepONet to explore the loss landscape and escape local minima via swapping. Compared to DeepONets trained with state-of-the-art gradient-based algorithms (e.g., Adam), the proposed Bayesian DeepONet greatly improves the training convergence for noisy scenarios and accurately estimates the uncertainty. To further reduce the high computational cost of the reLD training of DeepONets, we propose (1) an accelerated training framework that exploits the DeepONet's architecture to reduce its computational cost up to 25% without compromising performance and (2) a transfer learning strategy that accelerates training DeepONets for PDEs with different parameter values. Finally, we illustrate the effectiveness of the proposed Bayesian DeepONet using four parametric PDE problems.
97 MATHEMATICS AND COMPUTING↗
Massively parallel first-principles simulation of electron dynamics in materials
Here we present a highly scalable, parallel implementation of first-principles electron dynamics coupled with molecular dynamics (MD). By using optimized kernels, network topology aware communication, and by fully distributing all terms in the time-dependent Kohn–Sham equation, we demonstrate unprecedented time to solution for disordered aluminum systems of 2000 atoms (22,000 electrons) and 5400 atoms (59,400 electrons), with wall clock time as low as 7.5 s per MD time step. Despite a significant amount of non-local communication required in every iteration, we achieved excellent strong scaling and sustained performance on the Sequoia Blue Gene/Q supercomputer at LLNL. We obtained up to 59% of the theoretical sustained peak performance on 16,384 nodes and performance of 8.75 Petaflop/s (43% of theoretical peak) on the full 98,304 node machine (1,572,864 cores). Lastly, scalable explicit electron dynamics allows for the study of phenomena beyond the reach of standard first-principles MD, in particular, materials subject to strong or rapid perturbations, such as pulsed electromagnetic radiation, particle irradiation, or strong electric currents.
36 MATERIALS SCIENCE↗
Multi-variable mathematical models for the air-cathode microbial fuel cell system
This research adopted the version control system into the model construction for the single chamber air-cathode microbial fuel cell (MFC) system, to understand the interrelation of biological, chemical, and electrochemical reactions. The anodic steady state model was used to consider the chemical species diffusion and electric migration influence to the MFC performance. In the cathodic steady state model, the mass transport and reactions in a multi-layer, abiotic cathode and multi-bacteria cathode biofilm were simulated. Transport of hydroxide was assumed for cathodic pH change. This assumption is an alternative to the typical notion of proton consumption during oxygen reduction to explain elevated cathode pH. The cathodic steady state model provided the power density and polarization curve performance results that can be compared to an experimental MFC system. Another aspect we considered was the relative contributions of platinum catalyst and microbes on the cathode to the oxygen reduction reaction (ORR). We found simulation results showed that the biocatalyst in a cathode that includes a Pt/C catalyst likely plays a minor role in ORR, contributing up to 8% of the total power calculated by the models.
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Structural, electronic and magnetic properties of carbon doped boron nitride nanowire: Ab initio study
Using spin-polarized density functional theory calculations, we demonstrated that carbon doped boron nitride nanowire (C-doped BNNW) has diverse electronic and magnetic properties depending on position of carbon atoms and their percentages. Our results show that only when one carbon atom is situated on the edge of the nanowire, C-doped BNNW is transformed into half-metal. The calculated electronic structure of the C-doped BNNW suggests that doping carbon can induce localized edge states around the Fermi level, and the interaction among localized edge states leads to semiconductor to half-metal transition. Overall, the bond reconstruction causes of appearance of different electronic behavior such as semiconducting, half-metallicity, nonmagnetic metallic, and ferromagnetic metallic characters. The formation energy of the system shows that when a C atom is doped on surface boron site, system is more stable than the other positions of carbon impurity. Our calculations show that C-doped BNNW may offer unique opportunities for developing nanoscale spintronic materials.
36 MATERIALS SCIENCE↗
Point model equations for neutron correlation counting: Extension of Böhnel's equations to any order
Various methods of autocorrelation neutron analysis may be used to extract information about a measurement item containing spontaneously fissioning material. The two predominant approaches being the time correlation analysis (that make use of a coincidence gate) methods of multiplicity shift register logic and Feynman sampling. The common feature is that the correlated nature of the pulse train can be described by a vector of reduced factorial multiplet rates. We call these singlets, doublets, triplets etc. Within the point reactor model the multiplet rates may be related to the properties of the item, the parameters of the detector, and basic nuclear data constants by a series of coupled algebraic equations – the so called point model equations. Solving, or inverting, the point model equations using experimental calibration model parameters is how assays of unknown items is performed. Currently only the first three multiplets are routinely used. In this work we develop the point model equations to higher order multiplets using the probability generating functions approach combined with the general derivative chain rule, the so called Faà di Bruno Formula. Explicit expression up to 5th order are provided, as well the general iterative formula to calculate any order. This study represents the first necessary step towards determining if higher order multiplets can add value to nondestructive measurement practice for nuclear materials control and accountancy.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Quantum complexity in gravity, quantum field theory, and quantum information science
Quantum complexity quantifies the difficulty of preparing a state or implementing a unitary transformation with limited resources. Applications range from quantum computation to condensed matter physics and quantum gravity. Here, we seek to bridge the approaches of these fields, which define and study complexity using different frameworks and tools. We describe several definitions of complexity, along with their key properties. In quantum information theory, we focus on complexity growth in random quantum circuits. In quantum many-body systems and quantum field theory (QFT), we discuss a geometric definition of complexity in terms of geodesics on the unitary group. In dynamical systems, we explore a definition of complexity in terms of state or operator spreading, as well as concepts from tensor-networks. We also outline applications to simple quantum systems, quantum many-body models, and QFTs including conformal field theories (CFTs). Finally, we explain the proposed relationship between complexity and gravitational observables within the holographic anti-de Sitter (AdS)/CFT correspondence.
Baiguera, Stefano [Istituto Nazionale di Fisica Nu↗
A pathway for every product? Tools to discover and design plant metabolism
The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. In this work, we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.
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