Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix MultiplicationLocality-driven integration is a pervasive computational pattern in quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or integral…Xinran Wei, Yan Pan, Fusong Ju et al.·May 13, 2026SaveLearn
FusionRCG: Orchestrating Recursive Computation Graphs across GPU Memory HierarchiesEvaluating high-dimensional integrals via deep hierarchical recurrences is a dominant cost in quantum chemistry. While CPUs manage these efficiently, GPUs suffer a critical mismatch: limited…Yihong Zhang, Xinran Wei, Junshi Chen et al.·May 13, 2026SaveLearn
Quantifying Multidimensional Transport Effects on Permeability Inference in FLiBe Systems Using a Validation-Informed Modeling FrameworkPermeability of hydrogen isotopes in molten salts is commonly inferred from permeation experiments using simplified one-dimensional interpretations, which may not capture the coupled transport…Huihua Yang, Abhishek Saraswat, Weiyue Zhou et al.·May 12, 2026SaveLearn
Reduction of finite-size effects for second-order Møller-Plesset perturbation theory with singularity subtractionSecond-order Moller-Plesset perturbation theory (MP2) provides accurate correlation energies for periodic systems but suffers from finite-size errors (FSEs) that have inverse volume scaling due to…Stephen Jon Quiton, Juan D. F. Pottecher, Martin Head-Gordon et al.·May 12, 2026SaveLearn
Structure Matters: A Scale-Resolved Numerical Operando Approach for Lithium-Sulfur BatteriesLithium-Sulfur batteries (LSBs) are believed to have a high potential for aerospace applications due to their high gravimetric energy density. However, despite decades of research and advances, they…Max Okraschevski, Torben Prill, Paul Maidl et al.·May 12, 2026SaveLearn
Neural-ISAM: A hybrid in-situ machine learning approach for complex manifold-based combustion models in LES of turbulent flamesManifold-based combustion models decrease the cost of turbulent combustion simulations by projecting the thermochemical state onto a lower-dimensional manifold, allowing the thermochemical state to…S. Trevor Fush, Israel J. Bonilla, Michael B. Schroeder et al.·May 11, 2026SaveLearn
Constitutive Priors for Inverse DesignWith recent advances in material synthesis and additive manufacturing, material systems can be designed to achieve prescribed mechanical responses. An important class of such problems is the inverse…Jinkyo Han, Bahador Bahmani·May 10, 2026SaveLearn
Bayesian Reasoning for Physics Informed Neural NetworksWe introduce an evidence-driven Bayesian formulation of physics-informed neural networks that enables automatic optimization of loss weights between PDE residuals, boundary conditions, and…Krzysztof M. Graczyk, Kornel Witkowski·May 10, 2026SaveLearn
Nonlinear GENERIC-Embedded Neural Networks (N-GENNs): Learning GENERIC dynamics with non-quadratic dissipation potentialsWe introduce Nonlinear GENERIC-Embedded Neural Networks (N-GENNs), a deep learning framework for discovering evolution equations of systems governed by the nonlinear GENERIC formalism (General…Vojtěch Votruba, Zequn He, Weilun Qiu et al.·May 9, 2026SaveLearn
A Time-Domain Method of Auxiliary Sources for Analyzing Transient Electromagnetic Interactions with GSTC-Modeled MetasurfacessThis paper presents a time domain (TD) formulation for modeling the transient electromagnetic response of two-dimensional (2D) metasurfaces using the Method of Auxiliary Sources (MAS) combined with…Minas Kouroublakis, Nikolaos L. Tsitsas, Yehuda Leviatan·May 8, 2026SaveLearn
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum MicromechanicsPhysics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simulations. Its practical use, however, is often…Hamidreza Eivazi, Henning Wessels·May 8, 2026SaveLearn
foap4: Adaptive mesh refinement with OpenACC, MPI, and p4estGPUs and other accelerators are increasingly used for scientific computing. In the future, we want to add GPU support to parallel adaptive mesh refinement (AMR) codes written in Fortran. To…Jannis Teunissen, Héctor R. Olivares Sánchez, Jesse Vos et al.·May 8, 2026SaveLearn
Data-driven reconstruction of band dispersion and quantum geometry via Koopman dynamical mode decompositionWe present a data-driven framework for reconstructing band structures using Koopman operator analysis and dynamic mode decomposition (Koopman-DMD). Instead of deriving spectra from an explicit…Yiming Pan, Jinze He, Jiapeng Yang et al.·May 7, 2026SaveLearn
An MRI-informed poromechanical model for organ-scale prediction of glioma growthGliomas constitute one of the most aggressive and heterogeneous forms of brain tumors, posing major challenges for understanding their biology and developing effective treatments. Animal models…Meryem Abbad Andaloussi, Stephane Urcun, David A. Hormuth et al.·May 6, 2026SaveLearn
CDFCI: High-Performance Parallel Software for Many-Body Large-Scale Eigenvalue ProblemsCDFCI is a shared-memory parallel numerical program for computing low-lying eigenpairs of large-scale, non-relativistic fermionic Hamiltonians. The software is designed to handle a broad class of…Yuejia Zhang, Zhe Wang, Jianfeng Lu et al.·May 6, 2026SaveLearn
Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networksComposite materials exhibit strongly hierarchical and anisotropic properties governed by coupled mechanisms spanning constituents, plies, laminates, structures, and manufacturing history. This…Haizhou Wen, Elham Kiyani, Gang Li et al.·May 4, 2026SaveLearn
Lattice-Spring Analogy for Isotropic ElasticityThis study introduces an innovative Isotropic Elastic Lattice Spring Model (IELSM) that addresses the fundamental limitation of classical lattice spring models: the constraint of fixed Poisson's…D. M. LI, Meng-Cheng HE·May 3, 2026SaveLearn
Unsupervised Learning of Quantum Phase Transitions for Bose-Hubbard lattice systemsCharacterizing quantum many-body phase structure is a major goal for quantum simulation. Here, we employ an unsupervised learning approach based on diffusion maps to learn phase transitions in…Bihui Zhu·May 1, 2026SaveLearn
MuDirac 1.3.0: A Sustainable Software Tool for Calculating Ground State Nuclear Properties Using Muonic X-Ray MeasurementsThe nuclear charge radius is one of the most fundamental quantities of the atomic nucleus. It can be deduced from a combination of experimental measurements of…Leandro Liborio, Milan Kumar, Subindev Devadasan et al.·May 1, 2026SaveLearn
Computation of frequency- and time-domain Jacobians in optical tomography with Monte Carlo simulationsSignificance: Jacobians, or spatially resolved sensitivity profiles, are central to image reconstruction in model-based optical tomography of biological tissue. Although Monte Carlo (MC) simulations…Pauliina Hirvi, Jaakko Olkkonen, Qianqian Fang et al.·Apr 30, 2026SaveLearn
Kolmogorov-Sinai entropies identify optimal observables for prediction and dynamics reconstruction in chaotic systemsChoosing the optimal observable to model dynamical systems for which we do not know the driving equations is nearly always an ad hoc art. Takens' Delay Embedding Theorem guarantees a diffeomorphism…Maximilian Topel·Apr 30, 2026SaveLearn
Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic SimulationsFirst-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning Interatomic…Gabriel de Miranda Nascimento, Marc L. Descoteaux, Laura Zichi et al.·Apr 28, 2026SaveLearn
Accelerating finite-element-based projector augmented-wave density functional theory calculations with scalable GPU-centric computational methodsAccurate large-scale Kohn-Sham density functional theory (DFT) calculations are essential for modeling complex material systems, including interfaces, defects, nanoclusters, and twisted…Kartick Ramakrishnan, Phani Motamarri·Apr 28, 2026SaveLearn
Basic linear algebra methods for quantum problemsMaking new methods for quantum problems often relies on using basic operations in linear algebra. Often these routines are hidden behind well-known libraries that have been optimized over decades.…Aaron Dayton, Kiana Gallagher, Sarah E. Huber et al.·Apr 28, 2026SaveLearn
Deterministic Realization of Classical Dissipation on Quantum ComputersLattice Boltzmann (LB) on quantum devices must reconcile unitary gate evolution with the dissipative collision step. In the multiple-relaxation-time (MRT) class, we work in the common setting…Muhammad Idrees Khan, Sauro Succi, Hua-Dong Yao·Apr 28, 2026SaveLearn