dpti: An Automated Thermodynamic Integration Workflow for Phase Diagram Calculations with Machine Learning Interatomic PotentialsThermodynamic integration (TI) is a widely used approach for computing free energies and phase diagrams. However, TI calculations driven by machine learning interatomic potentials (MLIPs) remain…Fengbo Yuan, Xin Zhong, Donghao Zheng et al.·Jul 6, 2026SaveLearn
Accelerating Multi-scale Simulations of Nuclear Components via PCYS Interpolation TablesZirconium alloy core components in nuclear reactors, such as spacer grids and fuel cladding, undergo anisotropic dimensional changes driven by coupled irradiation creep and growth. While…Fabrizio Aguzzi, Martin S. Armoa, Cesar I. Pairetti et al.·Jul 6, 2026SaveLearn
Hyper Boris integrators for kinetic plasma simulations and their connection to 3D rotation representationsParticle-in-cell (PIC) simulation is one of the most important research tools in theoretical plasma physics. To solve the motion of charged particles, the Boris method (a.k.a. the Boris…S. Zenitani, T. N. Kato·Jul 5, 2026SaveLearn
Solving Boolean Satisfiability Problems Using A Hypergraph-based Probabilistic ComputerBoolean Satisfiability (SAT) problems are critical in fields such as artificial intelligence and cryptography, where efficient solutions are essential. Conventional probabilistic solvers often…Yihan He, Ming-Chun Hong, Wanli Zheng et al.·Jul 5, 2026SaveLearn
Restoring the uniform density limit in Perdew-Zunger self-interaction correctionThe Perdew-Zunger self-interaction correction (PZ-SIC) makes approximate density functionals exact for all one-electron densities, but sacrifices exactness for uniform densities. I show that an…Benjamin G. Janesko·Jul 4, 2026SaveLearn
Atomic Design Transformer: xTB-Validated 3D Molecule Generation from ScaffoldsWe present an SE(3)-invariant transformer for 3D-molecule generation, the Atomic Design Transformer (ADT). ADT places atoms one at a time, autoregressively. The SE(3) invariance is achieved by…Takao Kotani·Jul 4, 2026SaveLearn
Machine Learning Hamiltonians are Accurate Energy-Force PredictorsRecently, machine learning Hamiltonian (MLH) models have gained traction as fast approximations of electronic structures such as orbitals and electron densities, while also enabling direct evaluation…Seongsu Kim, Chanhui Lee, Yoonho Kim et al.·Jul 4, 2026SaveLearn
Verification of a sequential thermo-poroelasticity formulation in PFLOTRANWe present the verification of a thermo--hydrologic--mechanical capability implemented within the PFLOTRAN framework, with emphasis on benchmark-based assessment of the THM implementation. The…J. Al Kubaisy, G. E. Hammond, S. Karra et al.·Jul 3, 2026SaveLearn
Conditional Normalizing Flow for Gas-Surface Scattering from Thermal to Hypersonic VelocitiesAccurate aerodynamic modeling of satellites in very low Earth orbit (VLEO) requires gas-surface interaction (GSI) models that capture the full velocity spectrum from thermal to orbital speeds.…Miklas Schütte, Stephen Hocker, Hansjörg Lipp et al.·Jul 3, 2026SaveLearn
Modelling Gas-Phase Reaction Kinetics with Guided Particle Diffusion SamplingPhysics-guided sampling with diffusion priors has recently shown strong performance in solving complex systems of partial differential equations (PDEs) from sparse observations. However, these…Andrew Millard, Zheng Zhao, Henrik Pedersen·Jul 3, 2026SaveLearn
Verification and Performance Assessment of NuDEAL, a GPU-Accelerated Deterministic Transport Framework on Unstructured MeshesHigh-fidelity neutronic analyses of advanced reactors require deterministic transport solvers capable of handling complex unstructured geometries while maintaining computational efficiency. This work…Kyung Min Kim, Jaeuk Im, Han Gyu Lee et al.·Jul 2, 2026SaveLearn
libwignernj: a reusable C/C++/Fortran/Python library for exact Wigner symbols and related coefficientsWe describe libwignernj, a freely available, BSD-licensed library that evaluates Wigner 3j, 6j, and 9j symbols, Clebsch--Gordan, Racah W, and Fano X coefficients, and Gaunt coefficients over both…Susi Lehtola·Jul 2, 2026SaveLearn
Grounded autonomous scrutiny at scale: emergent critique from reproduction of published computational physics papersAutonomous LLM agents now produce complete research artifacts in machine-learning sandboxes, but real computational physics is harder: experiments are first-principles calculations against…Haonan Huang·Jul 2, 2026SaveLearn
From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational PhysicsWhile large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher. What…Haonan Huang·Jul 2, 2026SaveLearn
Walking on Spheres and Talking to Neighbors: Variance Reduction for Laplace's EquationWalk on Spheres algorithms leverage properties of Brownian Motion to create Monte Carlo estimates of solutions to a class of elliptic partial differential equations. We propose a new caching strategy…Michael Czekanski, Benjamin Faber, Margaret Fairborn et al.·Jul 2, 2026SaveLearn
Learning Effective Soliton Dynamics from Scattering DataThe inverse scattering transform (IST) provides the standard theoretical framework for deriving soliton dynamics. Traditionally, such derivations have been of an analytical, rather than data-driven,…Seth Minor, Vanja Dukic, David M. Bortz·Jul 1, 2026SaveLearn
Lanczos Method for QRPA Strength Functions in Atomic NucleiWe present a symmetric Lanczos method for computing charge-changing QRPA strength functions in atomic nuclei. Starting from the finite-amplitude-method formulation of the QRPA linear-response…Dong Min Roh, Chao Yang, Jonathan Engel et al.·Jul 1, 2026SaveLearn
LSR-Net: Long-Short-Range Operator Learning for Pattern Dynamics on ManifoldsWe propose the Long-Short-Range Neural Network (LSR-Net), an extensible operator-learning framework for predicting pattern dynamics on planar domains, spherical surfaces, and general manifolds. The…Qian Serena Hou, Zecheng Gan·Jul 1, 2026SaveLearn
Monte Carlo Physics-informed Neural Networks for Inverse Multiscale Heat Conduction Problems via the Phonon Boltzmann Transport EquationInferring thermal fields and thermophysical properties from limited measurements is a fundamental challenge in micro- and nanoscale heat conduction, where the classical Fourier law breaks down and…Qingyi Lin, Chuang Zhang, Xuhui Meng et al.·Jul 1, 2026SaveLearn
MADField: Multi-fidelity Amortized Density Field for Adsorption in Nanoporous MaterialsHigh-throughput computational screening of nanoporous materials for gas storage and separation requires fast and accurate characterization of adsorption equilibrium. Particle-based grand canonical…Yoonho Kim, Seongsu Kim, Sungsoo Ahn et al.·Jul 1, 2026SaveLearn
Pulgon-tools: A toolkit for analysing and harnessing symmetries in quasi-1D systemsPulgon-tools is an open-source software package providing building blocks for the analysis and modeling of quasi-one-dimensional (quasi-1D) periodic systems based on line-group theory. While mature…Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen et al.·Jul 1, 2026SaveLearn
How Physical Dynamics Shape the Properties of Ising Machines: Evaluating Oscillators vs. Bistable Latches as Ising SpinsIsing machines exploit the natural dynamics of physical systems to minimize the Ising Hamiltonian and thereby address computationally hard combinatorial optimization problems. This paradigm has…Abir Hasan, Nikhil Shukla·Jul 1, 2026SaveLearn
A Scoping Review of Physics Informed Machine Learning for Wave Propagation Modeling in SeismologyBackground: Standard numerical methods accurately simulate seismic waves but are computationally expensive, particularly for inverse problems. Machine learning approaches have been proposed as…Óscar Rincón-Cardeño, Gregorio Pérez-Bernal, Silvana Montoya-Noguera et al.·Jun 30, 2026SaveLearn
P3MaZe: a Mass-Zero constrained-dynamics formulation of particle-mesh electrostaticsWe introduce P3MaZe, a real-space particle-mesh electrostatic method that combines the standard short-range/long-range decomposition of Particle-Particle Particle-Mesh (P3M) electrostatics with the…Federica Troni, Violette Gontran, Davide Grassano et al.·Jun 30, 2026SaveLearn
Relaxation of Incommensurate Structures via Quantum ModelsAccurately modeling structural relaxation in incommensurate systems is intrinsically challenging due to the absence of global translational symmetry. In this work, we develop a variational quantum…Mengfan Tu, Huajie Chen, Daniel Massatt·Jun 30, 2026SaveLearn