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At least 937 records · Page 52

Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression

We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

FedEFsz: Fair Cross-Silo Federated Learning System With Error-Bounded Lossy Compression

Cross-Silo federated learning systems have been identified as an efficient approach to scaling DNN training across geographically-distributed data silos to preserve the privacy of the training data. Communication efficiency and fairness are two major issues that need to be both satisfied when federated learning systems are deployed in practice. Simultaneously guaranteeing both of them, however, is exceptionally difficult because simply combining communication reduction and fairness optimization approaches often causes non-converged training or drastic accuracy degradation. Here, to bridge this gap, we propose FedEFsz. On the one hand, it integrates the state-of-the-art error-bounded lossy compressor SZ3 into cross-silo federated learning systems to significantly reduce communication traffic during the training. On the other hand, it achieves a high fairness (i.e., rather consistent model accuracy and performance across different clients) through a carefully designed heuristic algorithm that can tune the error-bound of SZ3 for different clients during the training. Extensive experimental results based on a GPU cluster with 65 GPU cards show that FedEFsz improves the fairness across different benchmarks by up to 60.88% and meanwhile reduces the communication traffic by up to 315×.

Cross-Silo Federated Learning Systems↗

High‐Temperature Mechanical Behavior of Single‐Crystal FeCrAl Alloy Under In Situ Micropillar Compression

FeCrAl cladding is one of the candidate materials for the near‐term accident‐tolerant fuel technologies under development. Research on high‐temperature mechanical behaviors of single‐crystal FeCrAl alloy is rather limited. Previous studies have reported the mechanical property of low‐index orientation in single‐crystal FeCrAl alloy at room temperature. However, the critical resolved shear stress to activate slip systems can be orientation and temperature dependent. Here, single‐crystal grains in a coarse‐grained FeCrAl alloy with different crystallographic orientations are selected to preferentially activate {110}<111> slip systems or {112}<111> slip systems. Micropillars are fabricated in the selected single‐crystal grains and tested at elevated temperatures in situ in a scanning electron microscope. The critical resolved shear stresses of {110}<111> slip systems and {112}<111> slip systems are determined at various temperatures. The critical resolved shear stress shows a temperature dependence and orientation independence. This study provides important insight for understanding the deformation mechanisms of FeCrAl alloys at elevated temperatures.

Sun, Tianyi↗

Optically Induced Picosecond Lattice Compression in the Dielectric Component of a Strongly Coupled Ferroelectric/Dielectric Superlattice

Above-bandgap femtosecond optical excitation of a ferroelectric/dielectric BaTiO 3 /CaTiO 3 superlattice leads to structural responses that are a consequence of the screening of the strong electrostatic coupling between the component layers. Time-resolved X-ray free-electron laser diffraction shows that the structural response to optical excitation includes a net lattice expansion of the superlattice consistent with depolarization-field screening driven by the photoexcited charge carriers. The depolarization-field-screening-driven expansion is separate from a photoacoustic pulse launched from the bottom electrode on which the superlattice is epitaxially grown. The distribution of diffracted intensity of superlattice X-ray reflections indicates that the depolarization-field-screening-induced strain includes a photoinduced expansion in the ferroelectric BaTiO 3 and a contraction in CaTiO 3 . The magnitude of expansion in BaTiO 3 layers is larger than the contraction in CaTiO 3 . The difference in the magnitude of depolarization-field-screening-driven strain in the BaTiO 3 and CaTiO 3 components can arise from the contribution of the oxygen octahedral rotation patterns at the BaTiO 3 /CaTiO 3 interfaces to the polarization of CaTiO 3 . The depolarization-field-screening-driven polarization reduction in the CaTiO 3 layers points to a new direction for the manipulation of polarization in the component layers of a strongly coupled ferroelectric/dielectric superlattice.

36 MATERIALS SCIENCE↗

Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction

In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection-based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low-dimensional spaces, in which solutions are represented. In the proposed approach, low-dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics.

fracture mechanics↗

Searches for direct slepton production in the compressed-mass corridor in $\sqrt{\textrm{s}}$ = 13 TeV pp collisions with the ATLAS detector

This paper presents searches for the direct pair production of charged light-flavour sleptons, each decaying into a stable neutralino and an associated Standard Model lepton. The analyses focus on the challenging ``corridor'' region, where the mass difference, $Δm$, between the slepton ($\tilde{e}$ or $\tildeμ$) and the lightest neutralino ($\tildeχ^{0}_{1}$) is less or similar to the mass of the $W$ boson, $m(W)$, with the aim to close a persistent gap in sensitivity to models with $Δm \lesssim m(W)$. Events are required to contain a high-energy jet, significant missing transverse momentum, and two same-flavour opposite-sign leptons ($e$ or $μ$). The analysis uses $pp$ collision data at $\sqrt{s} = 13$ TeV recorded by the ATLAS detector, corresponding to an integrated luminosity of 140 fb$^{-1}$. Several kinematic selections are applied, including a set of boosted decision trees. These are each optimised for different $Δm$ to provide expected sensitivity for the first time across the full $Δm$ corridor. The results are generally consistent with the Standard Model, with the most significant deviations observed with a local significance of 2.0 $σ$ in the selectron search, and 2.4 $σ$ in the smuon search. While these deviations weaken the observed exclusion reach in some parts of the signal parameter space, the previously present sensitivity gap to this corridor is largely reduced. Constraints at the 95% confidence level are set on simplified models of selectron and smuon pair production, where selectrons (smuons) with masses up to 300 (350) GeV can be excluded for $Δm$ between 2 GeV and 100 GeV.

hadron-hadron scattering↗