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At least 1,315 records · Page 73

Pausing ultrafast melting by timed multiple femtosecond-laser pulses

An intense femtosecond-laser excitation of a solid induces highly nonthermal conditions. In materials like silicon, laser-induced bond-softening leads to a highly incoherent ionic motion and eventually nonthermal melting. But is this outcome an inevitable consequence, or can it be controlled? Here, we performed ab initio molecular dynamics simulations of crystalline silicon after timed multiple femtosecond-laser pulse excitations with fluence above the nonthermal melting threshold. Our results demonstrate an excitation mechanism that pauses nonthermal melting and creates a metastable state instead, with an electronic structure similar to the ground state. This mechanism can be generalized to other materials, potentially enabling structural and/or electronic transitions to metastable phases in the high-excitation regime. In addition, our approach could be used to switch off nonthermal contributions in experiments, allowing reliable electron-phonon coupling constants to be obtained more easily.

47 OTHER INSTRUMENTATION↗

Dual-unitary shadow tomography

We introduce a classical shadow tomography scheme based on dual-unitary brick-wall circuits termed "dual-unitary shadow tomography" (DUST). For this we study operator spreading and Pauli weight dynamics in one-dimensional qubit systems, evolved by random two-local dual-unitary gates arranged in a brick-wall structure, ending with a final measurement layer. We do this by deriving general constraints on the Pauli weight transfer matrix and specializing to the case of dual-unitarity. We first show that dual-unitaries must have a minimal amount of entropy production. Remarkably, we find that operator spreading in these circuits have a rich structure resembling that of relativistic quantum field theories, with massless chiral excitations that can decay or fuse into each other, which we call left- or right-movers. We develop a mean-field description of the Pauli weight in terms of $\rho(x,t)$, which represents the probability of having nontrivial support at site $x$ and depth $t$ starting from a fixed weight distribution. We develop an equation of state for $\rho(x,t)$, and simulate it numerically using Monte Carlo simulations. Lastly, we demonstrate that the fast-thermalizing properties of dual-unitary circuits make them better at predicting large operators than shallow brick-wall Clifford circuits. Our results are robust to finite-size effects due to the chirality of dual-unitary brick-wall circuits.

97 MATHEMATICS AND COMPUTING↗

Development of multi-scale computational frameworks to solve fusion materials science challenges

Over the past two decades, the US-DOE has funded multiple projects that rely on high-performance computing and exascale computing platforms to accelerate scientific discoveries and address grand scientific challenges, such as harnessing fusion energy. In this article, we review in detail one of these efforts aimed at enhancing our capability to model plasma-facing materials subject to plasma and high-energy ion/neutron irradiation. The plasma surface interactions project has built a multi-scale modeling framework where many of the plasma- and high-energy ion/neutron irradiation-induced effects occurring in tungsten are explored. Here, this knowledge is used to develop atomistically-informed, high-fidelity continuum and meso-scale models that can be validated against experiments. We review the developments within this project, with attention to experimental validation efforts, and specifically highlight activities associated with: helium bubble bursting and equation of state, and hydrogen-helium interactions in tungsten; atomistically-informed model development for beryllium-tungsten material mixing; coupling of scrape-of-layer plasma, sheath and material models; and coupling of stochastic cluster-dynamics and crystal plasticity models to address radiation effects in tungsten under stress. Finally, we present how the project is preparing for future computational architectures, for instance through efforts to adapt atomistic methods to exascale computing.

36 MATERIALS SCIENCE↗

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Computational and Systems Biology Advances to Enable Bioagent Agnostic Signatures

Enumerated threat agent lists have long driven biodefense priorities. The global SARS-CoV-2 pandemic demonstrated the limitations of searching for known threat agents as compared to a more agnostic approach. Recent technological advances are enabling agent-agnostic biodefense, especially through the integration of multi-modal observations of host-pathogen interactions directed by a human immunological model. Although well-developed technical assays exist for many aspects of human-pathogen interaction, the analytic methods and pipelines to combine and holistically interpret the results of such assays are immature and require further investments to exploit new technologies. In this manuscript, we discuss potential immunologically based bioagent-agnostic approaches and the computational tool gaps the community should prioritize filling.

59 BASIC BIOLOGICAL SCIENCES↗

Optimal strategies for optical quantum memories using long-lived noble-gas spins

Nuclear spins of noble gases exhibit exceptionally long coherence times and can potentially serve as a long-lived storage medium for quantum information. We analyze and compare the performance of two mechanisms for mapping the quantum state of light onto the collective spin state of noble gases. The first mechanism utilizes collisional exchange with the electronic spin state of metastable noble-gas atoms, while the second relies on spin-exchange collisions with ground-state alkali-metal atoms. We describe the operation of an optical quantum memory relying on these two mechanisms using a compact model and study strategies that optimize the memory storage efficiency. Through numerical simulations, we identify optimal sequences for storing optical signals with different signal bandwidths and electronic spin relaxation rates. This work highlights the qualitative difference between the two approaches for using noble gases as long-lived quantum memories at noncryogenic conditions and outlines the regimes in which they are expected to be efficient.

atomic ensemble↗

Europium-doped barium chloride storage phosphor plate synthesized by pulsed laser deposition

In computed radiography, scattering of the stimulation light by the storage phosphor crystals in the imaging plates negatively impacts spatial resolution. Storage phosphor plates with thinner phosphor layers have been developed to reduce scattering distance and increase spatial resolution, although at the expense of reduced x-ray absorption. The authors hypothesize that a transparent or translucent nanostructured film, containing a much higher percentage of storage phosphor crystals than achievable in bulk glass-ceramic materials made by conventional methods, may have acceptable photostimulated luminescence efficiency and imaging performance characteristics greater than commercial imaging plates. Films were produced via pulsed laser deposition by alternating target materials of the storage phosphor BaCl 2 :Eu 2+ and either silica or silicon. X-ray diffraction and photoluminescence characterization were conducted to confirm the presence of BaCl 2 :Eu 2+ crystallites. The films were able to store optical data and be read out to produce x-ray radiographs demonstrating 50-mu m spatial resolution. The performance of the experimental storage phosphor plates was compared to commercially available imaging plates. The authors demonstrate for the first time the synthesis of a glass-ceramic imaging plate for computed radiography by pulsed laser deposition.

36 MATERIALS SCIENCE↗

SU-E-T-314: The Application of Cloud Computing in Pencil Beam Scanning Proton Therapy Monte Carlo Simulation

Purpose: Monte Carlo simulation plays an important role for proton Pencil Beam Scanning (PBS) technique. However, MC simulation demands high computing power and is limited to few large proton centers that can afford a computer cluster. We study the feasibility of utilizing cloud computing in the MC simulation of PBS beams. Methods: A GATE/GEANT4 based MC simulation software was installed on a commercial cloud computing virtual machine (Linux 64-bits, Amazon EC2). Single spot Integral Depth Dose (IDD) curves and in-air transverse profiles were used to tune the source parameters to simulate an IBA machine. With the use of StarCluster software developed at MIT, a Linux cluster with 2–100 nodes can be conveniently launched in the cloud. A proton PBS plan was then exported to the cloud where the MC simulation was run. Results: The simulated PBS plan has a field size of 10×10cm{sup 2}, 20cm range, 10cm modulation, and contains over 10,000 beam spots. EC2 instance type m1.medium was selected considering the CPU/memory requirement and 40 instances were used to form a Linux cluster. To minimize cost, master node was created with on-demand instance and worker nodes were created with spot-instance. The hourly cost for the 40-node cluster was $0.63 and the projected cost for a 100-node cluster was $1.41. Ten million events were simulated to plot PDD and profile, with each job containing 500k events. The simulation completed within 1 hour and an overall statistical uncertainty of < 2% was achieved. Good agreement between MC simulation and measurement was observed. Conclusion: Cloud computing is a cost-effective and easy to maintain platform to run proton PBS MC simulation. When proton MC packages such as GATE and TOPAS are combined with cloud computing, it will greatly facilitate the pursuing of PBS MC studies, especially for newly established proton centers or individual researchers.

07 ISOTOPE AND RADIATION SOURCES↗

Introduction to the Special Issue on Software Tools for Quantum Computing: Part 1

Quantum computing is emerging as a remarkable technology that offers the possibility of achieving major scientific breakthroughs in many areas. Here, by leveraging the unique features of quantum mechanics, quantum computers may be instrumental in advancing many areas, including science, energy, defense, medicine, and finance. This includes solving complex problems whose solution lies well beyond the capacity of contemporary and even future supercomputers that are based on conventional computing technologies. As a foundation for future generations of computing and information processing, quantum computing represents an exciting area for developing new ideas in computer science and computational engineering.

97 MATHEMATICS AND COMPUTING↗

Introduction to the Special Issue on Software Tools for Quantum Computing: Part 2

Quantum computing is emerging as a remarkable technology that offers the possibility of achieving major scientific breakthroughs in many areas. By leveraging the unique features of quantum mechanics, quantum computers may be instrumental in advancing many areas, including science, energy, defense, medicine, and finance. This includes solving complex problems whose solution lies well beyond the capacity of contemporary and even future supercomputers that are based on conventional computing technologies. As a foundation for future generations of computing and information processing, quantum computing represents an exciting area for developing new ideas in computer science and computational engineering.

97 MATHEMATICS AND COMPUTING↗

Science & Technology Review: The Road to Exascale Computing

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world. The Department of Energy’s Exascale Computing Project (ECP) and Lawrence Livermore’s RADIUSS (Rapid Application Development via an Institutional Universal Software Stack) initiative benefit from strategically developed software tools. The front cover shows a simulation of advection under twisting rotation that uses high-order finite elements from Livermore’s Modular Finite Element Methods (MFEM) software library and GLVis visualization tool. On the back cover, the logo (also created with GLVis) for the MFEM project illustrates the curved mesh and sub-element resolution used in high-order simulations. MFEM and GLVis are key components of the ECP’s co-design Center for Efficient Exascale Discretizations (CEED) and RADIUSS. MFEM is also part of ECP’s Extreme-Scale Scientific Software Development Kit (xSDK).

97 MATHEMATICS AND COMPUTING↗

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗

Dynamic Workflows for Routine Materials Discovery in Surface Science

The rising application of informatics and data science tools for studying inorganic crystals and small molecules has revolutionized approaches to materials discovery and driven the development of accurate machine learning structure/property relationships. In this paper, we discuss how informatics tools can accelerate research, and we present various combinations of workflows, databases, and surrogate models in the literature. This paradigm has been slower to infiltrate the catalysis community due to larger configuration spaces, difficulty in describing necessary calculations, and thermodynamic/kinetic quantities that require many interdependent calculations. We present our own informatics tool that uses dynamic dependency graphs to share, organize, and schedule calculations to enable new, flexible research workflows in surface science. This approach is illustrated for the large-scale screening of intermetallic surfaces for electrochemical catalyst activity. Similar approaches will be important to bring the benefits of informatics and data science to surface science research. Lastly, we provide our perspective on when to use these tools and considerations when creating them.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine learning-based discovery of vibrationally stable materials

The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials. Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials, namely thermodynamic stability. However, the vibrational stability, which is another aspect of synthesizability, of new materials is not known. Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable. Here, a dataset of vibrational stability for ~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials. This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.

36 MATERIALS SCIENCE↗