🔬 Category

physics.comp-ph

1141 papers

Computational physics bridges the gap between abstract theory and real-world observation by using powerful computers to solve complex physical problems. This field allows scientists to simulate everything from the collision of subatomic particles to the swirling dynamics of galaxies, offering insights that traditional experiments alone cannot provide.

On Gist.Science, we continuously process every new preprint in this category from arXiv to make these breakthroughs accessible to everyone. Each entry is accompanied by both a clear, plain-language explanation and a detailed technical summary, ensuring that researchers and curious readers alike can grasp the significance of the latest findings without getting lost in dense equations.

Below are the latest papers in computational physics, curated to keep you at the forefront of this rapidly evolving discipline.

Efficient Implementation of the Spin-Free Renormalized Internally-Contracted Multireference Coupled Cluster Theory

This paper reports an efficient, parallelized, spin-free implementation of the renormalized internally-contracted multireference coupled cluster with singles and doubles (RIC-MRCCSD) method within the ORCA software suite, which achieves computational costs comparable to single-reference CCSD while avoiding high-order reduced density matrices and demonstrating both scalability to large systems and accuracy comparable to various perturbation theories.

Kalman Szenes, Riya Kayal, Kantharuban Sivalingam, Robin Feldmann, Frank Neese, Markus Reiher2026-04-24🔬 physics

Watts-per-Intelligence Part II: Algorithmic Catalysis

This paper establishes a thermodynamic theory of algorithmic catalysis within the watts-per-intelligence framework, proving that task-specific speed-ups are fundamentally limited by the algorithmic mutual information between the substrate and task descriptor, with a minimum thermodynamic cost for information installation that determines the energy-efficient deployment horizon for reusable computational structures.

Elija Perrier2026-04-24🔢 math

Chaos Gated Tunneling Drives Molecular Reactivity in Astrophysical Environments

This paper introduces a chaos-diagnostic framework combining multireference electronic structure theory, Adiabatic Gauge Potentials, and Random Matrix Theory to demonstrate how quantum chaos suppression at transition states enhances proton-transfer tunneling in ultracold astrophysical environments, thereby offering a new metric for refining ion-molecule reaction models in planetary atmospheres.

Saptarshi G. Dastider, K. Prashant, P. Shruti, C. Sudheesh, Jobin Cyriac2026-04-24🔬 physics

Modulation Effects of Atmospheric Environmental Conditions on Mesoscale Convective Systems over Tropical Oceans

This study utilizes an observational dataset and Random Forest modeling to quantify how atmospheric conditions, particularly moisture convergence, instability, and water vapor, nonlinearly control the spatial and seasonal variability of mesoscale convective systems over tropical oceans, explaining up to 50% of their frequency and precipitation variance.

Huaiping Wang, Qiu Yang2026-04-24🔬 physics

Two-Way Feedback Mechanisms between the Madden-Julian Oscillation and Mesoscale Convective Systems

This study utilizes satellite-based indices and long-term MCS tracking data to demonstrate a robust two-way feedback mechanism where the Madden-Julian Oscillation organizes mesoscale convective systems through environmental modulation, while the collective upscale impacts of these systems actively reinforce the MJO's circulation and support its eastward propagation.

Haobo Yang, Qiu Yang2026-04-24🔬 physics

Accelerating point defect simulations using data-driven and machine learning approaches

This paper reviews data-driven and machine learning approaches, particularly descriptor-based models and interatomic potentials trained on DFT data, that accelerate point defect simulations in solid-state materials by enabling rapid, quantum-mechanically accurate predictions of properties like formation energies and vibrational free energies for high-throughput screening and experimental integration.

Arun Mannodi-Kanakkithodi, Menglin Huang, Prashun Gorai, Seán R. Kavanagh2026-04-24🔬 cond-mat.mtrl-sci

Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models

This paper introduces DiffSRDA, a computationally efficient, uncertainty-aware data assimilation framework based on denoising diffusion models that generates high-resolution ensemble analyses from low-resolution forecasts and sparse observations, achieving performance comparable to high-cost methods while supporting training-free adaptation to changing sensor layouts.

Aditya Sai Pranith Ayapilla, Kazuya Miyashita, Yuki Yasuda, Ryo Onishi2026-04-24🔬 physics

A High-Order Nodal Galerkin Formulation for the Müller Equation: Bypassing Divergence Conformity via Kernel Cancellation

This paper introduces a high-order nodal Galerkin formulation for the Müller boundary integral equation that bypasses the need for divergence-conforming basis functions by exploiting kernel cancellation to reduce hypersingularities, thereby enabling robust, superlinear convergence through a metric-weighted tangent frame and specialized preconditioning.

Yao Luo2026-04-24🔬 physics