1258 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.
The paper introduces SAP-X2C, a computationally efficient two-component relativistic Hamiltonian that incorporates two-electron picture-change effects via Lehtola's superposition of atomic potentials to achieve accuracy comparable to complex mean-field methods while maintaining size-intensivity for extended systems.
The paper introduces a highly efficient one-way shooting algorithm for transition path sampling in overdamped stochastic systems that guarantees the acceptance of every proposed reactive trajectory through a reweighting scheme, thereby enabling the effective study of difficult-to-access processes like CO clathrate hydrate formation.
This paper introduces a multitask surrogate model for neutron star equations of state that achieves near-perfect physical validity classification and high-precision regression of key stellar properties while providing distribution-free, certified uncertainty estimates through the novel application of Mondrian conformal prediction.
This study presents a machine learning framework for predicting steady-state flow through porous media, demonstrating that the Fourier Neural Operator (FNO) outperforms convolutional autoencoders and U-Nets by achieving high accuracy, significant computational speedups over traditional CFD, and mesh-invariant properties ideal for topology optimization.
This paper demonstrates that combining machine learning with gold-standard CCSD(T) electronic structure theory enables the first fully converged, quantitative prediction of the ion pairing free energy for CaCO in water, resolving long-standing challenges in accurately capturing enthalpic and entropic effects for complex aqueous systems.
This paper demonstrates that Oscillator Ising Machines outperform Bistable Latch Ising Machines in solving combinatorial optimization problems because the configuration-dependent stability of oscillators allows for the selective destabilization of high-energy states, whereas the uniform stability of latches limits their computational efficiency.
This paper introduces a Physics-Informed Neural Operator (PINO) surrogate model that accelerates the retention analysis of Ferroelectric Vertical NAND devices by over 10,000 times compared to traditional TCAD simulations while maintaining physical accuracy, thereby enabling efficient optimization of device designs against charge detrapping and ferroelectric depolarization.
This paper demonstrates that machine learning models trained to predict the two-electron reduced density matrix (2-RDM) can accurately surrogate correlated wavefunction methods, enabling coupled-cluster-quality electronic structure calculations for large solvated systems at a fraction of the conventional computational cost.
This paper presents a GPU-accelerated transient Electromagnetic-Thermal-Mechanical co-simulation solver that enables full-scale, non-homogenized early-stage design of advanced packages, overcoming the limitations of conventional steady-state methods by accurately capturing dynamic signal-induced stress and thermal events to prevent costly late-stage failures.
This paper presents a robust semi-analytical pseudo-spectral method utilizing mapped associated Legendre polynomials and an advanced exponential time differencing scheme to efficiently and accurately simulate rotating, stratified flows in unbounded cylindrical domains by overcoming the numerical stiffness typically caused by strong shear and fast wave forces.