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 proposes an Optimization-based Bound Tightening (OBBT) framework that uses interval arithmetic and McCormick relaxations to provide guaranteed, sampling-free uncertainty bounds for non-identifiable groundwater models, while addressing challenges like non-physical rotational flow through specific sign and irrotationality constraints.
This paper introduces a combined approach using sparse regression-based cluster expansion and semigrand-canonical Monte Carlo sampling to efficiently model the intercalation thermodynamics of disordered rocksalt cathodes, successfully reproducing experimental voltage profiles and elucidating the redox contributions of Mn and oxygen in LiMnNbOF.
This paper introduces BayesMBAR, a Bayesian generalization of the multistate Bennett acceptance ratio (MBAR) method that computes free energy posterior distributions to provide more accurate uncertainty estimates and allow for the incorporation of prior knowledge, such as surface smoothness, into free energy calculations.
This paper presents a consensus-based framework that unifies phase space exploration with posterior-residual-based adaptive sampling to solve the minimax optimization problem of jointly constructing surrogate models and generating samples for high-dimensional energy landscapes, effectively enabling the efficient approximation of free energy surfaces in complex biomolecular systems.
This study utilizes machine learning and diffusion-based structure prediction to reveal that amorphous lithium difluorophosphate (\ce{LiPO2F2}), a key solid-electrolyte interphase component, exhibits high ionic conductivity due to structural disorder and abundant interstitial defects, suggesting that amorphous mixed-anion phases are the primary fast-ion pathways in Li-ion batteries.
This paper investigates the impact of soft versus hard boundary enforcement techniques on the accuracy and training efficiency of physics-informed neural networks (PINNs) for elastodynamic problems, demonstrating that while hard enforcement of traction conditions on implicit geometries reduces runtime, it often trades off against solution accuracy compared to soft enforcement.
This paper introduces an agent-guided multi-fidelity learning framework that employs a structural agent to diagnose numerical instabilities in GW-Bethe-Salpeter calculations and applies machine learning corrections to accurately predict quasiparticle and excitonic properties in strained MoS2-WS2 bilayers, demonstrating that explicit detection of numerical fragility is essential for reliable surrogate modeling of excited-state materials.
PDE-Agents is an LLM-orchestrated multi-agent framework that automates finite element simulations through natural language, demonstrating that GraphRAG-augmented reasoning significantly enhances task success and material property fidelity compared to non-augmented baselines.
This paper presents an all-electron numerical atom-centered orbital implementation of the dynamical Bethe-Salpeter equation for extended systems, which incorporates dynamical screening effects and is validated through applications to molecular crystals like naphthalene.
This paper introduces Covalent Field Theory (CFT), a framework that resolves longstanding ambiguities in molecule-surface interactions by redefining chemical affinity as a continuous interfacial property rather than a discrete geometric attribute, thereby providing a theoretical basis for active site emergence, linear scaling relations, and Brønsted-Evans-Polanyi correlations across complex surfaces.