General & Applied Physics · Physics & Astronomy
Computational Physics
The newest papers in Computational Physics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
244 papers, from 31 August 2026 to 5 November 2026: 204 arXiv preprints and 40 OpenAlex records.
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Newest papers, page 9
Native implementation of the machine-learned Skala exchange-correlation functional in CP2K: Unified one-centre reconstruction for molecular and condensed-phase calculations
We implement the machine-learned Skala exchange-correlation (XC) functional natively in CP2K using its Gaussian and plane-wave (GPW) and Gaussian and augmented-plane-wave (GAPW) methods.E3J: An Efficient and Open-Source Backend for Euclidean Equivariant Operations on GPU and TPU
We present e3j, a fast Euclid-equivariance backend for geometric deep learning applications with JAX bindings for GPU and TPU.GameDev: GPU-accelerated model for dust evolution
Grain growth and fragmentation through dust collisions in protoplanetary disks are strongly coupled with dust dynamics.Let CSP Be Your ANCHOR: Adaptive Crystal Search over Frozen Structure Priors
De novo crystal generation (DNG) models decide where to search in composition space and how to generate structures with one set of weights.MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception
Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic.Quantum-Computing Self-Consistent Kohn-Sham DFT: Plane-Wave-Orthonormalized Orbitals with a Dual-Basis Quantum Eigensolver
We present a first-quantization, all-electron, full-potential Kohn-Sham density functional theory method for quantum computers, based on a classically optimized quantum-circuit eigensolver for the…Conserved and relaxing modes enable polynomial-success coherent Carleman lattice Boltzmann evolution
Carleman linearization represents nonlinear lattice Boltzmann collision dynamics in a form amenable to quantum block encoding, but low postselection probabilities in existing population-space…On the Limits of Univariate Deep Learning for Significant Wave Height Forecasting
This study conducts a systematic hyperparameter search across five deep learning architectures, DLinear, LSTM, PatchTST, ResAttLstm, and Mamba2, and nine context lengths (1-168 h) for single-station…Scaling Density Functional Theory with Gaussian Splatting
Density functional theory (DFT) strikes a practical balance between accuracy and computational cost in many problems of computational chemistry and materials science.SHRAV: State-Hypothesis-Reason-Action-Verify Framework for Physical Modeling and Inverse Design
Physical modeling and inverse design require computation that can continue from reusable state.SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials
Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples…Optimal limits on weak integrability breaking and protected thermal memory near qutrit exchange
Although integrability does not universally require a continuous one-site symmetry, we rigorously prove that every jointly analytic, regular Yang-Baxter deformation of the qutrit exchange…Simulation of a Battery Cell on Quantum Computers: Reactions & Transport
Simulations of electrochemical materials and systems accelerate technological progress, but are still limited by computational power.Differentiable astrophysics at scale: solving and differentiating ODE ensembles on the GPU
Astronomers increasingly fit their models with gradient-based methods, such as Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters.The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning
In scientific machine learning, $Δ$-learning trains models on residual errors relative to physical baselines, assuming that more accurate baselines with smaller residual scales inherently improve…Scale-Vector Alignment: A Scale-Aware Framework for Spatially Resolved Morphological Similarity in Astronomical Images
Astronomical maps made with different tracers are not expected to have identical morphology.Benchmarking higher-ranking multipoles and polarizability tensors for small molecular systems
Molecular properties govern how molecules interact with one another or external fields.Predictive Uncertainty for Neural CAE Surrogates
Neural surrogates can substantially accelerate computer-aided engineering (CAE) workflows, but their use in design requires uncertainty estimates that remain meaningful across varying geometries…Complete Neural Electronic Initialization Accelerates Materials DFT
We present the first complete machine learning method for accelerating plane-wave density functional theory (DFT) in materials under the projector augmented wave (PAW) formalism.Nonlinearly Causal General-Relativistic Two-Fluid Dissipative Magnetohydrodynamics
We present a formulation of general-relativistic (GR) 19-moment dissipative magnetohydrodynamics (MHD), capable of handling all first-order dissipative terms (viscosity, heat conductivity…
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