physics.comp-ph papers | Gist.Science

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.

Stable, Fast, and Accurate Kohn-Sham Inversion in Gaussian Basis for Open Shell Molecular and Condensed Phase Systems via Density Matrix Penalization

This paper presents a robust and efficient density matrix-based Kohn-Sham inversion method formulated entirely within a Gaussian basis that utilizes Lowdin orthogonalization and analytical penalty potentials to overcome the limitations of conventional approaches, achieving superior accuracy in reproducing target electron densities for both open-shell molecular and condensed phase systems.

Ziwei Chai, Sandra Luber2026-03-24🔬 physics

Development and large-scale benchmarks of a protein-ligand absolute binding free energy toolkit

The paper introduces Felis, an open-source, automated toolkit that, when paired with the ByteFF force field, enables high-throughput, zero-shot absolute binding free energy calculations with accuracy comparable to state-of-the-art relative binding free energy methods across diverse protein-ligand datasets.

Yu Liu, Ailun Wang, Yu Xia, Zhi Wang, Wen Yan2026-03-24🔬 physics

Unidirectionality of spin waves in Synthetic Antiferromagnets

This paper demonstrates through experiments and modeling that symmetric CoFeB/Ru/CoFeB synthetic antiferromagnets in a scissors state exhibit strong frequency non-reciprocity in their acoustical spin waves, enabling unidirectional energy transfer for wavevectors parallel to the applied magnetic field.

F. Millo, J. -P. Adam, C. Chappert, J. -V. Kim, A. Mouhoub, A. Solignac, T. Devolder2026-03-23⚛️ quant-ph

Symmetry of the dissipation of surface acoustic waves by ferromagnetic resonance

This study investigates the coupling between surface acoustic waves and ferromagnetic resonance in a CoFeB film on a LiNbO3 substrate, revealing an unexpected 2-fold symmetry in SAW energy absorption that is attributed to weak in-plane uniaxial anisotropy rather than dipolar interactions.

Florian Millo, Rafael Lopes Seeger, Claude Chappert, Aurélie Solignac, Thibaut Devolder2026-03-23⚛️ quant-ph

Geometry-Driven Segregation in Periodically Textured Microfluidic Channels

This paper demonstrates that periodically textured microchannel walls can passively align elongated microparticles along the centerline through geometry-driven shear gradients, offering a robust framework for designing microfluidic devices to sort or focus anisotropic particles across various flow regimes.

Fatemeh S. Ahmadi, Hossein Hamzehpour, Reza Shaebani2026-03-23🔬 physics

Oxygen-vacancy quantum spin defects in silicon carbide

This study definitively identifies the long-elusive PL5 and PL6 spin defects in 4H-SiC as oxygen-vacancy centers in $kh$ and $hh$ configurations, respectively, by combining oxygen ion implantation, isotopic labeling with $^{17}$ O, and theoretical calculations to enable their deterministic engineering for quantum applications.

Yu Chen, Qi Zhang, Mingzhe Liu, Junda Wu, Jinpeng Liu, Xin Zhao, Jingyang Zhou, Pei Yu, Shaochun Lin, Yuanhong Teng, Wancheng Yu, Ya Wang, Changkui Duan, Fazhan Shi2026-03-23🔬 cond-mat.mes-hall

Matched Asymptotic Expansions-Based Transferable Neural Networks for Singular Perturbation Problems

This paper introduces MAE-TransNet, a transferable neural network method that integrates matched asymptotic expansions with specialized pre-trained neurons to efficiently and accurately solve singular perturbation problems across various dimensions by effectively capturing boundary layer characteristics while outperforming existing neural network approaches in accuracy and computational cost.

Zhequan Shen, Lili Ju, Liyong Zhu2026-03-23🔬 physics

Complete finite-size scaling theory of Renyi thermal entropy for second, first and weak first order quantum phase transitions

This paper establishes a unified finite-size scaling framework based on Renyi thermal entropy and its derivative to accurately distinguish between second-order, first-order, and weak first-order quantum phase transitions, offering a robust numerical tool that resolves long-standing ambiguities in identifying weak first-order transitions within finite-size simulations.

Zhe Wang, Yanzhang Zhu, Yi-Ming Ding, Zenan Liu, Zheng Yan2026-03-23🔬 cond-mat

Electrostatics in semiconducting devices II : Solving the Helmholtz equation

This paper introduces a robust and fast algorithm for solving self-consistent quantum-electrostatic problems in semiconducting devices by mapping them to a provably convergent Non-Linear Helmholtz equation, which is then iteratively refined to achieve the exact solution in just a few steps.

Antonio Lacerda-Santos, Xavier Waintal2026-03-23🔬 cond-mat.mes-hall

Virtual Sensing for Solder Layer Degradation and Temperature Monitoring in IGBT Modules

This paper demonstrates that machine learning-based virtual sensing can accurately estimate solder layer degradation and reconstruct full temperature maps in IGBT modules using limited physical sensors, achieving a mean absolute error of 1.17% for degraded area and a maximum relative error of 4.56% for surface temperature.

Andrea Urgolo, Monika Stipsitz, Hèlios Sanchis-Alepuz2026-03-23🔬 physics

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