General & Applied Physics · Physics & Astronomy
Chemical Physics
The newest papers in Chemical Physics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
160 papers, from 4 September 2026 to 5 November 2026: 160 arXiv preprints.
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Newest papers, page 8
SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials
Machine-learned interatomic potentials (MLIPs) for solid-liquid interfaces in advanced materials applications, e.g., electrochemistry, catalysis and corrosion, require training data that samples…Broadband Quantum Optical Storage with Chemically Engineered Molecular Eu$^\text{3+}$ Complex
Broadband quantum memory devices are essential elements for future quantum networks.Decoherence Tuning in Voltage-Driven Plasmonic Cavities
Strong light-matter coupling offers a powerful framework for modifying molecular structure, dynamics, and optical response, yet achieving this regime at the single-molecule level remains a central…Simulation of a Battery Cell on Quantum Computers: Reactions & Transport
Simulations of electrochemical materials and systems accelerate technological progress, but are still limited by computational power.The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning
In scientific machine learning, $Δ$-learning trains models on residual errors relative to physical baselines, assuming that more accurate baselines with smaller residual scales inherently improve…A Green-function unification of weak interactions with environmental dressing
Weak, non-covalent interactions such as electrostatics, induction, and dispersion are commonly treated as distinct physical mechanisms, often using separate models and approximations.An Exact Operator Formulation of Statistical Quasiparticle Theory for Nonlinear System-Bath Coupling
We develop an operator formulation of statistical quasiparticle theory for open quantum systems with nonlinear system-bath coupling, extending beyond the linear coupling setting of the standard…Benchmarking higher-ranking multipoles and polarizability tensors for small molecular systems
Molecular properties govern how molecules interact with one another or external fields.Rachel: A general-purpose language model directs and revises retrosynthetic routes
Retrosynthetic planning advances through decisions that reshape the remaining chemical problem: a locally plausible disconnection can leave precursors whose chemoselectivity constraints complicate…SPIBER: Reconstructing Free Energy Landscapes from Short, Unconverged Trajectories with Generative Flow Networks
Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables.Truncated automatic sparse differentiation for machine learning interatomic potentials
Machine learning interatomic potentials (MLIPs) learn the mapping from atomic positions to potential energy.Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization
Neural networks provide expressive representations for scientific computing.QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization…Machine learning kinetics from molecular dynamics data
Most molecular transitions occur on timescales far beyond direct molecular dynamics simulations.METALICA: METAdynamics and repLICA exchange for enhanced diffusion sampling
Many proteins function through transitions between conformational states, yet rare states are rarely sampled by diffusion models trained on an equilibrium ensemble, demanding better sampling methods.El Agente Potente: High-Throughput Agentic Atomistic Simulations
Foundational machine-learning interatomic potentials (MLIPs) are transforming atomistic simulations by achieving near-ab initio accuracy across large chemical spaces at a fraction of the…Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation
Designing polymers with targeted properties requires navigating vast chemical spaces from limited labeled data.Fixed-Dimensional Latent Flow for Generating Variable-Size 3D Molecules
In molecular discovery, molecule size is coupled to composition, structure, and other target properties.MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials
Universal machine-learning interatomic potentials (u-MLIPs) aim to generalize across diverse configurations.Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applications, such as vibrational analysis and…
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