903 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.
This paper introduces Jeffreys Flow, a robust generative framework that mitigates mode collapse in Boltzmann generators by distilling Parallel Tempering data via symmetric Jeffreys divergence, thereby enabling accurate and scalable sampling of complex, multi-modal physical systems.
This paper demonstrates that employing Centroidal Voronoi Tessellation (CVT) archives with ChemBERTa-2 and UMAP-derived chemical embeddings significantly outperforms traditional grid-based approaches in Multi-Objective MAP-Elites for discovering diverse, high-quality nonlinear optical molecules by efficiently navigating complex chemical spaces and balancing multiple competing objectives.
This paper presents an integrated machine learning framework that efficiently explores the high-dimensional composition space of refractory compositionally complex alloys (RCCAs) to predict phase stability and temperature-dependent mechanical properties, thereby accelerating the discovery of new high-temperature materials.
The paper introduces gyaradax, a high-performance JAX/CUDA solver for local flux-tube gyrokinetics that achieves significant speedups and native GPU acceleration over legacy Fortran codes like GKW, while demonstrating how agentic workflows guided by human expertise can rapidly translate complex physics simulations to enable advanced machine learning applications in plasma physics.
This paper introduces a Lagrangian-based technique that replaces the computationally expensive requirement of DFT calculations with a single coupled-perturbed Kohn-Sham calculation to efficiently generate unbiased atomic forces in ab initio Variational Monte Carlo, thereby improving their consistency with potential energy surfaces and accuracy relative to CCSD(T) benchmarks.
This paper introduces an atom-by-atom inverse design framework that integrates Nano-Topology Optimization with conditional diffusion models to overcome continuum limitations by explicitly accounting for crystal symmetry and surface physics, thereby enabling the discovery of high-performance metallic nanostructures like aluminum nanocantilevers and nanopillars.
This paper presents a "nowcast transform" method to adjust Gutenberg-Richter statistics within regional earthquake ensembles for improved nowcasting and forecasting of large earthquake exceedance probabilities, demonstrating its consistency and application to the Los Angeles region following the 1994 Northridge earthquake.
This paper challenges the century-old Lindemann-Hinshelwood mechanism by demonstrating that barrierless termolecular reactions, such as ion-atom recombination, are fundamentally governed by direct three-body dynamics rather than sequential bimolecular encounters, a finding that resolves long-standing theory-experiment discrepancies and establishes a new framework for understanding these processes across various scientific fields.
This paper investigates a discrete-time quantum walk where a detector is periodically removed and randomly relocated, revealing that while both unrestricted (Model 1) and restricted (Model 2) relocation schemes mimic a semi-infinite walk for long intervals, they exhibit distinct spreading behaviors and quantum-enhanced occupation probability ratios in the rapid-relocation regime.
This paper introduces a pedagogical computational framework that reconstructs band formation in periodic media by simulating time-domain wave propagation in finite systems, thereby bridging the gap between abstract Bloch theory and intuitive physical concepts like scattering and interference.