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 5
Quadrature-detected zero-to-ultralow-field nuclear magnetic resonance
We present the concept of quadrature-detected zero-to-ultralow-field nuclear magnetic resonance (QD ZULF NMR).On the Theoretical Limits of Learning Unconstrained Interatomic Potentials
Machine-learned interatomic potentials (MLIPs) accelerate molecular dynamics (MD) simulations by learning the energies and forces that govern atomic motion.NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo
Neural-network variational Monte Carlo provides an accurate route to solving the electronic Schrödinger equation, but many neural wavefunctions are optimized for a single nuclear geometry, requiring…Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals
Machine learning methods are increasingly used to predict chemical properties directly from the molecular structure to bypass the often prohibitive cost of quantum mechanical calculations.Non-Perturbative Vibrational Excitation by Arbitrary Electric Fields from Classical Phase-Space Dynamics
We present a non-perturbative framework for the quantum treatment of vibrational excitation in molecular systems driven by electric fields with arbitrary time dependence.Gas-phase chemistry of $\mathrm{C}_3\mathrm{H}_3^+$: a key building block for carbon mass growth in space
The gas-phase chemistry of $\mathrm{C}_3\mathrm{H}_3^+$ plays a central role in the growth of carbonaceous species across a wide range of environments, from combustion systems to interstellar media.MAST: Motif-Augmented Diffusion with Search Tree for Spectroscopic Molecular Structure Elucidation
Elucidating molecular structures from spectra is a foundational problem in chemical and materials characterization, yet remains challenging due to spectral ambiguity and the vast molecular space.MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?
Large language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations.Predicted state-to-state collisional rate coefficients from neural networks
Molecular spectra observed in the interstellar medium are commonly analyzed using non-local thermodynamic equilibrium models, which requires accurate state-to-state collisional rate coefficients.PEACE: Covariant learning of nonadiabatic manifolds with parity-resolved Hamiltonians
Nonadiabatic molecular dynamics provides mechanistic insight into light-driven processes and informs the design of molecules and materials for solar energy conversion, photocatalysis and photo…Spurious zero-dispersion continua in full $(2,q)$-positivity: A linear hierarchy-depth lower bound from an exactly solvable counting operator
Variational two-particle reduced-density-matrix methods impose hierarchies of semidefinite constraints.Cotunneling-Assisted Redistribution of Nonpassivity in a Photosynthetic Junction
We investigate how cotunneling affects passive-state ordering in a Photosystem II reaction center modelled as a nonequilibrium molecular junction.Physics-Aligned Electronic Ground-State Learning Improves Generalization
Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generalize out-of-distribution.LLM-Assisted Generation of Transparent, Open-Source Multiphysics Models of Electrochemical Devices
Multiphysics continuum models are powerful tools for studying electrochemical devices, enabling in silico reactor design and resolution of local pH, potential, and concentration fields that govern…NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning
Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching quantum-chemical accuracy at a fraction of the computational cost.Coherence-assisted quantum state preparation and thermal control through repeated interactions
We investigate the relaxation dynamics and steady state of a qubit interacting with a coherent environment through an energy-conserving flip-flop interaction within the Markovian…Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics
This position paper argues that the generative molecular dynamics (GenMD) community is not properly evaluating its progress: current evaluations let a model match what is reported while being…Demonstration of Parallel Multi-QPU Execution for Fragment-Based Quantum Chemistry Using On-Premises Hardware
Fragment molecular orbital (FMO) quantum chemistry offers a natural route to parallel quantum computing: large molecular calculations can be decomposed into smaller quantum subproblems that can, in…Learning consistent molecular mechanics force fields from first principles
Classical force fields (FFs) remain the workhorse for large-scale simulations even as machine-learned interatomic potentials (MLIPs) approach ab initio accuracy.Information-Dense Synthesis for Molecular Discovery
Machine learning can accelerate molecular discovery by designing molecules and planning experiments.
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