769 papers
This paper introduces a scalable quantum-nuclei-corrected NMR crystallography approach (QNC-NMR) that leverages the machine-learning potential PET-MOLS to generate quantum ensembles, thereby significantly improving the accuracy of chemical shielding predictions for hydrogen-bonded protons and enabling applications to amorphous materials without empirical corrections.
This study presents a robust quantitative framework combining Mach-Zehnder interferometry with humidity-controlled confinement to precisely map the drying kinetics and internal concentration fields of water-glycerol droplets, successfully validating a diffusion-controlled evaporation model and extracting concentration-dependent transport properties while confirming that mass diffusion dominates over buoyancy-driven convection.
This paper proposes using a traveling, rotating magnetic field as a more energetically efficient and penetrating alternative to electric fields for suppressing axial losses in multi-mirror fusion machines, thereby enabling the achievement of fusion conditions in the central cell.
This paper presents a theoretical investigation identifying promising vibrational transitions in helical osmocene with significant parity-violating shifts, proposing a pathway for the first experimental detection of parity violation in a chiral molecule using ultra-precise mid-IR spectroscopy.
This paper demonstrates that transforming molecular Hamiltonians to the adiabatic representation to eliminate spin-orbit coupling inadvertently generates significant non-adiabatic couplings that must be explicitly included to avoid severe errors in rovibronic predictions, providing exact conditions for validity and practical diagnostics for when single-state approximations fail.
This feasibility study demonstrates that Sparse Probabilistic Evaluation (SPE), a computationally efficient method using a predefined error grid, achieves sufficient accuracy for probabilistic treatment planning in IMPT head and neck patients while maintaining clinically acceptable computation times.
This study investigates how students utilize generative AI in physics computational modeling, finding that while it aids planning and debugging, productive use requires limiting reliance to small steps and verifying outputs, whereas over-reliance hinders fundamental learning and necessitates pedagogical adjustments like emphasizing pre-coding planning and maintaining human support.
This paper reports on the early operation and single-phase commissioning of Proto-0, a small-scale dual-phase argon TPC at INFN Naples designed to validate key technologies for the upcoming DarkSide-20k experiment, with a specific focus on measuring scintillation light yield using calibration sources.
This paper reports recent advancements in GasPM technology aimed at mitigating photon-feedback-induced time-resolution degradation through improved beam tests with a high-speed digitizer and exploring the robustness of LaB photocathodes for future detector upgrades.
This paper introduces machine-learning potentials incorporating environment-dependent long-range electrostatic charges that improve training accuracy for diverse systems and enable the prediction of key dielectric properties, such as LO-TO splitting and dielectric constants, using only energy, force, and stress data.
This paper reports the discovery of pressure-induced band gap widening in carbon-chain-filled KFI zeolite and the synthesis of ultra-long cumulene chains within this framework, which exhibit a record-breaking superconducting transition temperature of approximately 62 K, offering new pathways for high-pressure semiconducting electronics and high-temperature superconductivity.
This paper presents a comprehensive experimental characterization of a YAG:Ce scintillator, detailing its light yield, temperature-dependent decay time, alpha quenching factors, and strong pulse-shape discrimination capabilities across a wide energy range to validate its suitability for reliable radiation detection and particle identification.
This paper introduces a self-consistent, density-of-states-weighted decoherence probe formalism that resolves limitations in existing charge transport models for DNA by generating physically accurate, energy-dependent scattering rates that avoid unphysical broadening and spurious energy levels.
This paper presents a replicable, low-cost framework for conducting hour-long "Learning Together" sessions that facilitate safe, structured, turn-based reflection on historical, cultural, and equity-related topics within physics institutes.
This paper introduces a rotation-invariant graph neural network that generalizes across arbitrary diffusion MRI acquisition protocols without retraining, enabling rapid and accurate brain microstructure estimation for clinical deployment.
This paper provides a comprehensive comparison of explicit relativistic particle pushers used in Particle-in-Cell simulations, demonstrating that a significant class of these schemes can be generalized to arbitrary high-order accuracy and evaluating the performance of their fourth-order variants against second-order counterparts.
This study evaluates Aurora, an AI weather model, and finds that while it achieves strong short-range (1–7 day) forecast skill for various weather extremes comparable to traditional methods, its ability to predict the intensity of these events degrades significantly beyond 7–10 days as predictions regress toward climatology, indicating that intrinsic atmospheric dynamics still limit the practical predictability horizon for deterministic AI extreme-event forecasting.
This paper introduces a reproducible hybrid quantum-classical framework that combines Gaussian kernel-based quantum-inspired feature mapping with normalized structural descriptors in a Deep Quantum Neural Network to achieve superior cross-context generalization and robustness in predicting residue-level pKa values compared to classical models.
The paper introduces Matlantis-PFP v8, a universal machine learning interatomic potential trained on the more accurate r2SCAN functional rather than PBE, which achieves systematically improved agreement with experimental data and high-accuracy references across diverse chemical domains without requiring domain-specific fine-tuning.
This paper resolves the well-known pathologies of the Lorentz Abraham Dirac equation by modeling charged particles as finite, deformable spheres with internal breathing modes, thereby deriving a causal radiation reaction force that eliminates pre-acceleration and runaway solutions while providing a mechanical interpretation of the Schott term as reversible internal energy storage.