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Evaluations of gadolinium isotopes 155 Gd and 157 Gd in the resolved resonance region were performed with the code SAMMY. The main objective for revising the resonance evaluations of these isotopes was to address issues related to critical benchmark results.
Attendees from Los Alamos National Laboratory (LANL) traveled from the US to Slovenia to attend the meetings in-person at the Jožef Stefan Institute. They were invited to serve on the TRG to review submitted benchmark evaluations prior to their approval for publication in the ICSBEP Handbook.
The HHL algorithm is a prominent quantum algorithm that offers exponential speedup over its classical counterparts for solving a system of linear equations. However, synthesizing and executing HHL circuits demand significant computational resources from both classical and quantum systems. In this paper, we benchmark the HHL algorithm using the optimized Quantum Phase Estimation (QPE) generation algorithm, LuGo \cite{lu2025lugo}, to enhance its scalability and efficiency. We leverage the National Energy Research Scientific Computing Center's (NERSC) Perlmutter supercomputer to evaluate the scalability of generating HHL circuits and to measure the time to simulate the generated circuits. Additionally, we provide a comprehensive analysis of the algorithm's performance on various state-of-the-art superconducting and trapped-ion quantum devices, including studies on qubit connectivity, fidelity comparisons, and hardware compatibility and robustness. Our results offer preliminary insights into potential practical applications of the HHL algorithm enabled by LuGo and the performance of various types of quantum hardware.
A number of new advanced reactor designs use TRI-structural ISO-topic (TRISO) fuel. The uranium enrichment for the TRISO fuel is in the 5- to 19.75-weight percent range, which is considered High-Assay Low Enriched Uranium (HALEU). The Nuclear Regulatory Commission and vendors both have a strong interest in benchmarking these advanced reactor designs and having confidence in the predictive capabilities of the computer codes used to model them. In the recent years, Serpent 2 gained several capabilities focused on the reactor applications making it attractive for many reactor vendors.
Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.
Description of impacts to Sandia NCS benchmark suite following implementation of ENDF/B-VIII.0 nuclear data library.
Description of impacts to Sandia NCS benchmark suite following implementation of ENDF/B-VIII.0 nuclear data library.
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Transition metal complexes have been widely used as catalysts or chromophores in artificial photosynthesis. Traditionally, they are employed in homogeneous settings. Despite their functional versatility and structural tunability, broad industrial applications of these catalysts are impeded by the limitations of homogeneous catalysis such as poor catalyst recyclability, solvent constraints (mostly organic solvents), and catalyst durability. Over the past few decades, researchers have developed various methods for molecular catalyst heterogenization to overcome these limitations. Here, in this review, we summarize recent developments in heterogenization strategies, with a focus on describing methods employed in the heterogenization process and their effects on catalytic performances. Alongside the in-depth discussion of heterogenization strategies, this review aims to provide a concise overview of the key metrics associated with heterogenized systems. We hope this review will aid researchers who are new to this research field in gaining a better understanding.
Since the weather is chaotic, it is necessary to forecast an ensemble of future states. Recently, multiple AI weather models have emerged claiming breakthroughs in deterministic skill. Unfortunately, it is hard to fairly compare ensembles of AI forecasts because variations in ensembling methodology become confounding and the baseline data volume is immense. We address this by scoring lagged initial condition ensembles—whereby an ensemble can be constructed from a library of deterministic hindcasts. This allows the first parameter‐free intercomparison of leading AI weather models' probabilistic skill against an operational baseline. Lagged ensembles of the two leading AI weather models, GraphCast and Pangu, perform similarly even though the former outperforms the latter in deterministic scoring. These results are elaborated upon by sensitivity tests showing that commonly used multiple time‐step loss functions damage ensemble calibration.
Density functional theory (DFT) and electron propagator theory (EPT) calculations were performed to study ground and excited electronic structures of alkali-metal (M) coordinated 9-crown-3, 24-crown-8, [2.1.1]cryptand, o-Me 2 -1.1.1, and 3 6 Adamanzane complexes. Each complex bears an expanded electron in the periphery and occupies diffuse 1p-, 1d-, 1f-type molecular orbitals (or superatomic 1P, 1D, 1F orbitals) in excited electronic states. The calculated superatomic shell model of the M(9-crown-3) 2 is 1S, 1P, 1D, 1F, 2S, 2P, 2D, 1G and it is held by all other complexes up to the studied 1F level. Due to the highly diffuse nature of the electron, the ionization energies of these complexes are significantly lower (1.6–2.0 eV) and hence these complexes belong to the superalkali category. Further, the ab initio EPT ionization energy and the excitation energies of the Li(9-crown-3) 2 were used to evaluate DFT errors associated with a series of exchange correlation functionals that span multiple rungs of Jacob's ladder (i.e., GGA, meta-GGA, global GGA hybrid, meta-GGA hybrid, range-separated hybrid, double-hybrid). Among these, the best performing functional is the range-separated hybrid CAM-B3LYP and the errors are within 6% of high-level ab initio EPT results. The accuracy of CAM-B3LYP is indeed transferable to similar complexes and hence the findings are expected to accelerate the progression of studies of Rydberg-type systems.
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