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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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Logical Majorana zero modes in a nanowire network
We present a scheme to use physical Majorana quasizero modes at each junction of a two-dimensional nanowire network to build a logical Majorana zero mode, the location of which is controllable through gate voltages. The wire network is a way to realize a recent proposal to imprint a Kekulé vortex pattern on a honeycomb lattice via gate voltages. We show that a specific type of junction, other than a naive Y or T junction, is needed to realize, without breaking time-reversal symmetry, an artificial “graphene” system with Majorana fermions instead of complex ones at each site. The junction we propose (i) traps exactly one physical Majorana (quasi)zero mode at each site of either a brick wall or honeycomb lattice and (ii) allows this mode to hybridize with all three neighboring sites. Using a lattice of these junctions and starting from an electronic, tight-binding model for the wires, we imprint the voltage patterns corresponding to the Kekulé vortex and observe the emergence of the logical Majorana zero mode at the vortex core. By suitable adiabatic modulations of gate voltages as a function of time, the positions of individual Kekulé vortices can be moved, and in this manner, non-Abelian braiding of the logical Majoranas bound to the vortex cores can be performed. We provide the range of parameters where these excitations could be realized experimentally.
Hierarchical Majoranas in a programmable nanowire network
We propose a hierarchical architecture for building “logical” Majorana zero modes using “physical” Majorana zero modes at the Y-junctions of a hexagonal network of semiconductor nanowires. Each Y-junction contains three “physical” Majoranas, which hybridize when placed in close proximity, yielding a single effective Majorana mode near zero energy. The hybridization of effective Majorana modes on neighboring Y-junctions is controlled by applied gate voltages on the links of the honeycomb network. This gives rise to a tunable tight-binding model of effective Majorana modes. We show that selecting the gate voltages that generate a Kekule vortex pattern in the set of hybridization amplitudes yields an emergent “logical” Majorana zero mode bound to the vortex core. The position of a logical Majorana can be tuned adiabatically, without moving any of the “physical” Majoranas or closing any energy gaps, by programming the values of the gate voltages to change as functions of time. Furthermore, a nanowire network supporting multiple such “logical” Majorana zero modes provides a physical platform for performing adiabatic non-Abelian braiding operations in a fully controllable manner.
Uncovering Magic Isotopes with the Power of HPC
Where do elements come from? How does the strong force bind subatomic particles into nuclei? What can scientists understand from nuclei with unusual proton–neutron ratios? Nuclear physicists at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) are seeking answers to such questions with the help of powerful supercomputers. The element tin is of particular interest to ORNL. In 2010, ORNL researchers discovered that the nucleus of a tin isotope, tin-132, was doubly “magic.” Isotopes are deemed magic when they have nucleons (positively charged proton particles or neutrally charged neutron particles) that complete a shell within the nucleus, making the magic isotopes much more strongly bound than those that are not magic. Isotopes with 2, 8, 20, 28, 50, 82, or 126 neutrons or protons are considered magic. A doubly magic isotope has two of these special numbers—one that describes its number of protons and one that describes its number of neutrons. Tin-132, for example, has 50 protons and 82 neutrons. The discovery of new magic isotopes can significantly affect the chart of nuclides, a table that orders the radioactive behaviors of isotopes. Because magic isotopes are more strongly bound, their structure impacts entire regions of the chart of nuclides and the limit of how many nuclei can exist.
A Communication-Optimal Framework for Contracting Distributed Tensors
Tensor contractions are extremely compute intensive generalized matrix multiplication operations encountered in many computational science fields, such as quantum chemistry and nuclear physics. Unlike distributed matrix multiplication, which has been extensively studied, limited work has been done in understanding distributed tensor contractions. In this paper, we characterize distributed tensor contraction algorithms on torus networks. We develop a framework with three fundamental communication operators to generate communication-efficient contraction algorithms for arbitrary tensor contractions. We show that for a given amount of memory per processor, our framework is communication optimal for all tensor contractions. We demonstrate performance and scalability of our framework on up to 262,144 cores of BG/Q supercomputer using five tensor contraction examples.
Novel phase diagram behavior and materials design in heterostructural semiconductor alloys
Structure and composition control the behavior of materials. Isostructural alloying is historically an extremely successful approach for tuning materials properties, but it is often limited by binodal and spinodal decomposition, which correspond to the thermodynamic solubility limit and the stability against composition fluctuations, respectively. We show that heterostructural alloys can exhibit a markedly increased range of metastable alloy compositions between the binodal and spinodal lines, thereby opening up a vast phase space for novel homogeneous single-phase alloys. We distinguish two types of heterostructural alloys, that is, those between commensurate and incommensurate phases. Because of the structural transition around the critical composition, the properties change in a highly nonlinear or even discontinuous fashion, providing a mechanism for materials design that does not exist in conventional isostructural alloys. The novel phase diagram behavior follows from standard alloy models using mixing enthalpies from first-principles calculations. Furthermore, thin-film deposition demonstrates the viability of the synthesis of these metastable single-phase domains and validates the computationally predicted phase separation mechanism above the upper temperature bound of the nonequilibrium single-phase region.
Accurate chemical master equation solution using multi-finite buffers
Here, the discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multiscale nature of many networks where reaction rates have a large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multifinite buffers for reducing the state space by $O(n!)$, exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be precomputed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multiscale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks.
Hacking Kilometer-Scale Models: A Participative Model for Climate Information
In May 2025, nearly 700 participants from all around the world coalesced at 10 regional nodes and a few satellite nodes to take part in a global hackathon of kilometer-scale (horizontal grid spacing < 10 km) regional and global Earth system models. Exciting science is emerging from these efforts, ranging across novel model analysis, new ways of integrating with satellite data, and emulation with machine learning. New technologies were trialed that enable the community to work in new and complementary ways to democratize access to global information at a local scale from a set of the world’s highest-resolution climate models. The hackathon demonstrated how exascale data can be organized to be accessible to anyone. Fundamentally, the community could apply these techniques and technologies to move toward more participative models for coproduction and delivery of diverse sources of climate information for climate scientists and citizens alike.
Designing an Optimal Sensor Network via Minimizing Information Loss
Optimal experimental design is a classic topic in statistics, with many well-studied problems, applications, and solutions. The design problem we study is the placement of sensors to monitor spatiotemporal processes, explicitly accounting for the temporal dimension in our modeling and optimization. We observe that recent advancements in computational sciences often yield large datasets based on physics-based simulations, which are rarely leveraged in experimental design. We introduce a novel model-based sensor placement criterion, along with a highly-efficient optimization algorithm, which integrates physics-based simulations and Bayesian experimental design principles to identify sensor networks that “minimize information loss” from simulated data. Our technique relies on sparse variational inference and (separable) Gauss-Markov priors, and thus may adapt many techniques from Bayesian experimental design. We validate our method through a case study monitoring air temperature in Phoenix, Arizona, using state-of-the-art physics-based simulations. Our results show our framework to be superior to random or quasi-random sampling, particularly with a limited number of sensors. We conclude by discussing practical considerations and implications of our framework, including more complex modeling tools and real-world deployments.
Laboratory Directed Research and Development Annual Report (FY 2013)
The mission of Pacific Northwest National Laboratory is to transform the world through courageous discovery and innovation. Every day, scientists and engineers at PNNL set out to fulfill the promise of that mission by advancing scientific discovery and delivering critical solutions to the challenges that face our nation and the world. The science and technology we work with, create, and improve upon inspires and enables the world to live prosperously, safely, and securely. Our investments in Laboratory Directed Research and Development are essential to our ability to advance our strategy and realize this mission. LDRD investments help us nurture science and technology capabilities while capitalizing on the breadth and depth of talent that our staff possess. This report describes how we conduct our LDRD program in compliance with DOE objectives and guidelines. We use rigorous internal and external peer review to maintain the scientific value and soundness of the research enabled by our LDRD program.
Laboratory Directed Research and Development Annual Report: Fiscal Year 2014
This report documents progress made on all LDRD-funded projects during fiscal year 2014.
Laboratory Directed Research and Development Annual Report for 2015
This report documents progress made on all LDRD_funded projects during fiscal year 2015.
OpenMC In Situ Source Convergence Detection
We designed and implemented an in situ version of particle source convergence for the OpenMC particle transport simulator. OpenMC is a Monte Carlo based-particle simulator for neutron criticality calculations. For the transport simulation to be accurate, source particles must converge on a spatial distribution. Typically, convergence is obtained by iterating the simulation by a user-settable, fixed number of steps, and it is assumed that convergence is achieved. We instead implement a method to detect convergence, using the stochastic oscillator for identifying convergence of source particles based on their accumulated Shannon Entropy. Using our in situ convergence detection, we are able to detect and begin tallying results for the full simulation once the proper source distribution has been confirmed. Our method ensures that the simulation is not started too early, by a user setting too optimistic parameters, or too late, by setting too conservative a parameter.
Supo Thermal Model Development II
This report describes the continuation of the Computational Fluid Dynamics (CFD) model of the Supo cooling system described in the report, Supo Thermal Model Development1, by Cynthia Buechler. The goal for this report is to estimate the natural convection heat transfer coefficient (HTC) of the system using the CFD results and to compare those results to remaining past operational data. Also, the correlation for determining radiolytic gas bubble size is reevaluated using the larger simulation sample size. The background, solution vessel geometry, mesh, material properties, and boundary conditions are developed in the same manner as the previous report. Although, the material properties and boundary conditions are determined using the appropriate experiment results for each individual power level.
Multiscale Modeling of Deformation, Fracture and Failure of Fusion Materials and Structures Final Report
The main objective of the research performed at UCLA under grant number DE-FG02-03ER54708 was to develop a physical understanding of the underlying mechanisms that control high-temperature deformation and fracture of fusion structural materials. The key aspect is the synergistic interaction between thermo-mechanical damage phenomena (e.g. creep, fatigue, and fracture), and irradiation damage phenomena (helium and hydrogen bubbles, and irradiation-induced defects). Another thrust area of the research was on the development of constitutive equations for the mechanical behavior of irradiated structures in fusion components (mainly the First Wall and Plasma-Facing components), and in utilizing them in descriptions in large-scale Finite Element Modeling of fusion structures. A parallel experimental effort on thermo-mechanical and plasma-induced damage is designed to validate and confront the developed theoretical and computational predictions. The models have been experimentally validated through experimental tests in simulated plasma transients on specially-designed W samples. This grant started at UCLA on November 14, 2003 and has had successive continuations till November 14, 2017. Research performed under the grant was to develop a range of hierarchical models for the post-elastic deformation, fracture and failure of fusion structural materials. The developed multiscale modeling approach has been based on rigorous mathematical, physical, and computational methods at the forefront of computational materials science. At the fundamental and smallest length scale (nm-μm), we developed advanced rate theory and Monte Carlo approaches to model microstructure evolution, non-equilibrium phase transformations, and dislocation-defect interactions. Microscopic and mesoscopic models of radiation hardening, ductile-to-brittle-transition, post-elastic deformation, plastic instabilities, and fracture processes are based on Dislocation Dynamics (DD) and Grain Growth Dynamics (GGD). At the continuum level, we focused on modeling fracture and failure mechanisms for Virtual Integrated Testing (VISTA), and for understanding the limits of both materials-by-design versus structural component design through advancing the new approach of multiscale-multiphysics. The overall research has been strongly coupled with the U.S. national experimental program to ascertain and verify the range of investigated phenomena. In the following, we give a summary of research progress supported by this grant since its inception till November 14, 2017. Detailed research results are available in our publications, listed in the publications section of this report.
Investigation of Structure and Reactivity Relationship in M-N-C Type Catalysts using Density Functional Tight Binding
Catalysts inhibition studies were performed to indisputably confirm the role of various metal, carbon, and nitrogen moieties in the individual steps of oxygen reduction reaction (ORR) on M-N-C catalysts. ORR activity was studied at University of New Mexico by rotating ring disk electrode method in the acidic electrolyte with the addition of Tris (tris(hydroxymethyl)-aminomethane) as inhibiting agent. To understand the interaction of Tris with different defects that exist in Fe-N-C materials and provide the support for the experimental data, we used density functional theory (DFT) and modeled the interaction of protonated Tris (TrisH) with Fe containingcenters (Fe-N 4 and Fe-N 2 C 2 ), pyridinic nitrogen, graphitic nitrogen, and pyrrolic nitrogen both as in plane and edge defects.
Advances in machine learned potentials for molecular dynamics simulation
No abstract provided.