physics.comp-ph papers | Gist.Science

Exotic Pressure-Driven Band Gap Widening in Carbon Chain-Filled KFI Zeolite and Its Pathway to High-Pressure Semiconducting Electronics and High-Temperature Superconductivity

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.

C. T. Wat, K. C. Lam, W. Y. Chan, C. P. Chau, S. P. Ng, W. K. Loh, L. Y. F. Lam, X. Hu, C. H. WongMon, 09 Ma🔬 physics

Accelerating Numerical Relativity Simulations with New Multistep Fourth-Order Runge-Kutta Methods

This paper introduces and validates new explicit fourth-order Multistep Runge-Kutta (MSRK) methods that accelerate Numerical Relativity simulations by reusing data from previous time steps to reduce intermediate stage evaluations, while providing a procedure to tune coefficients for maximizing stable time step sizes.

Lucas Timotheo Sanches, Steven Robert Brandt, Jay Kalinani, Liwei Ji, Erik SchnetterMon, 09 Ma🔬 physics

An Overview of Relativistic Particle Pushers and their Extension to Arbitrary Order Accuracy

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.

Holger SchmitzMon, 09 Ma🔬 physics

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants

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.

Dmitry Korogod, Alexander V. Shapeev, Ivan S. NovikovMon, 09 Ma🔬 physics

Spin-Orbit Induced Non-Adiabatic Dynamics: An Exact $\Omega$ -Representation

This paper demonstrates that transforming molecular Hamiltonians to the adiabatic $\Omega$ 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.

Ryan P. Brady, Sergei N. YurchenkoMon, 09 Ma🔬 physics

Machine Learning Based Mesh Movement for Non-Hydrostatic Tsunami Simulation

This study demonstrates that a machine learning-based mesh movement approach (UM2N) integrated into the Thetis software significantly accelerates and robustly enhances the accuracy of non-hydrostatic tsunami simulations for probabilistic coastal hazard assessment.

Yezhang Li, Stephan C. Kramer, Matthew D. PiggottMon, 09 Ma🔬 physics

Hybrid ensemble forecasting combining physics-based and machine-learning predictions through spectral nudging

This paper introduces a novel hybrid ensemble forecasting framework that uses spectral nudging to integrate machine-learned large-scale guidance with physics-based mesoscale dynamics, resulting in significant forecast skill improvements of up to two days in the tropics and enhanced tropical cyclone track predictions without compromising storm intensity or ensemble spread.

Inna Polichtchouk, Simon Lang, Sarah-Jane Lock, Michael Maier-Gerber, Peter DuebenMon, 09 Ma🔬 physics

Investigation of Aeroacoustics and In-flight Particle Transport in Thermal Spray Supersonic Jets

This study develops and validates a combined analytical and numerical framework linking thermal spray nozzle operating conditions to far-field aeroacoustic signatures and in-flight particle transport, demonstrating that acoustic monitoring can serve as a non-intrusive method for controlling and optimizing the thermal spray process.

D. Rahmat Samii, M. TembelyMon, 09 Ma🔬 physics

Drifting to Boltzmann: Million-Fold Acceleration in Boltzmann Sampling with Force-Guided Drifting

This paper introduces Drifting Models for molecular conformation generation, leveraging a novel Drifting Force Identity to incorporate physical force labels into one-step sampling, thereby achieving million-fold acceleration over traditional molecular dynamics while maintaining perfect structural validity and Boltzmann distribution accuracy.

Pipi HuMon, 09 Ma🔬 physics

FourierSpecNet: Neural Collision Operator Approximation Inspired by the Fourier Spectral Method for Solving the Boltzmann Equation

This paper introduces FourierSpecNet, a hybrid deep learning framework that integrates the Fourier spectral method to efficiently approximate the Boltzmann collision operator, achieving resolution-invariant learning, zero-shot super-resolution, and significant computational savings while maintaining accuracy across elastic and inelastic collision regimes.

Jae Yong Lee, Gwang Jae Jung, Byung Chan Lim, Hyung Ju HwangMon, 09 Ma🤖 cs.AI

JAWS: Enhancing Long-term Rollout of Neural Operators via Spatially-Adaptive Jacobian Regularization

The paper introduces JAWS, a probabilistic regularization strategy that dynamically modulates Jacobian constraints based on local physical complexity to resolve the contraction-dissipation dilemma, thereby enabling memory-efficient, short-horizon optimization to achieve superior long-term stability and accuracy in neural operator rollouts for dynamical systems.

Fengxiang Nie, Yasuhiro SuzukiMon, 09 Ma🤖 cs.AI

Towards Efficient and Stable Ocean State Forecasting: A Continuous-Time Koopman Approach

This paper demonstrates that the Continuous-Time Koopman Autoencoder (CT-KAE) serves as a lightweight, stable, and efficient surrogate model for long-horizon ocean state forecasting, outperforming autoregressive Transformer baselines by maintaining bounded errors and consistent large-scale statistics over 2083-day rollouts while enabling resolution-invariant predictions.

Rares Grozavescu, Pengyu Zhang, Mark Girolami, Etienne MeunierMon, 09 Ma🔬 physics.app-ph

Non-intrusive Monitoring of Sealed Microreactor Cores Using Physics-Informed Muon Scattering Tomography With Momentum Measurements

This paper introduces $\mu$ TRec, a physics-informed muon scattering tomography framework that significantly enhances the detection of missing fuel in sealed microreactor cores by reconstructing curved muon trajectories and incorporating momentum measurements, thereby outperforming conventional methods like PoCA in both sensitivity and speed under realistic cosmic-ray conditions.

Reshma Ughade, Stylianos ChatzidakisMon, 09 Ma🔬 physics.app-ph

On the Value of Tokeniser Pretraining in Physics Foundation Models

This paper demonstrates that pretraining tokenizers with an autoencoding objective before training dynamics models significantly enhances the computational efficiency and accuracy of physics foundation models, particularly when the pretraining data aligns with the downstream physical system.

Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana, Michael McCabe, Neil D. Lawrence, Shirley Ho, Miles CranmerMon, 09 Ma🔭 astro-ph

Frustrated supermolecules: the high-pressure phases of crystalline methane

Using molecular dynamics based on density functional theory, this study reveals that the complex high-pressure crystal phases of methane arise from the packing of specific supermolecular clusters (icosahedral and polyhedral) where a trade-off between efficient packing and suppressed rotational entropy, driven by orientation-dependent intermolecular interactions, explains the observed non-cubic symmetries and sluggish phase transitions.

Marcin Kirsz, Miguel Martinez-Canales, Ayobami D. Daramola, John S. Loveday, Ciprian G. Pruteanu, Graeme J AcklandMon, 09 Ma🔬 cond-mat.mtrl-sci

Entanglement Barriers from Computational Complexity: Matrix-Product-State Approach to Satisfiability

This paper demonstrates that the failure of the quantum-inspired Matrix Product State approach to solve 3-SAT via imaginary time propagation is fundamentally caused by classical computational complexity, specifically the hardness of the #3-SAT counting problem, which manifests as an entanglement barrier and necessitates superlinear non-stabilizer resources.

Tim Pokart, Frank Pollmann, Jan Carl BudichMon, 09 Ma⚛️ quant-ph

El Agente Cuantico: Automating quantum simulations

The paper introduces "El Agente Cuántico," a multi-agent AI system that automates complex quantum simulation workflows by translating natural-language scientific intent into validated computations across diverse software frameworks, thereby lowering technical barriers and enabling more autonomous exploration of quantum systems.

Ignacio Gustin, Luis Mantilla Calderón, Juan B. Pérez-Sánchez, Jérôme F. Gonthier, Yuma Nakamura, Karthik Panicker, Manav Ramprasad, Zijian Zhang, Yunheng Zou, Varinia Bernales, Alán Aspuru-GuzikMon, 09 Ma⚛️ quant-ph

Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning

This paper introduces a semidefinite machine learning framework that combines input convex neural networks with semidefinite programming to learn a data-driven, vertex-based approximation of the $N$ -representable two-electron reduced density matrix (2-RDM) boundary, enabling direct variational calculations with accuracy comparable to higher-order positivity constraints but at the computational cost of two-positivity methods.

Luis H. Delgado-Granados, David A. MazziottiMon, 09 Ma⚛️ quant-ph

Quantum chemistry based on classical mechanics inspired by simulated bifurcation

This paper introduces Simulated Bifurcation-based Configuration Interaction (SBCI), a classical mechanics-inspired algorithm that significantly reduces the computational cost of high-accuracy electronic structure calculations while maintaining reliability comparable to standard methods.

Fumihiko Aiga, Hayato GotoMon, 09 Ma⚛️ quant-ph

Prediction of Molecular Single-Photon Emitters: A Materials-Modelling Approach

This paper presents a computational framework that combines database analysis with microscopic modeling to predict and identify promising new molecular single-photon emitters, successfully benchmarking the approach on dibenzoterrylene and discovering novel candidates like a chiral emitter.

Erik Karlsson Öhman, Daqing Wang, R. Matthias Geilhufe + 1 more2026-03-11🔬 physics.app-ph

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