physics.chem-ph papers | Gist.Science

This collection explores the fascinating intersection where the laws of physics meet the complex machinery of chemistry. Here, researchers investigate how quantum mechanics governs molecular bonds, how light interacts with matter at the atomic scale, and how fundamental forces shape chemical reactions. It is a realm where abstract mathematical models collide with tangible substances to reveal the hidden mechanisms driving our material world.

On Gist.Science, we process every new preprint in this category directly from arXiv to make these discoveries accessible to everyone. Whether you are a seasoned expert or a curious reader, you will find both plain-language explanations and detailed technical summaries for each paper. Below are the latest contributions from the community pushing the boundaries of physical chemistry.

System-Bath Modeling in Vibrational Spectroscopy via Molecular Dynamics: A Machine Learning Framework for Hierarchical Equations of Motion (HEOM)

This paper presents a machine learning framework that utilizes classical molecular dynamics trajectories to construct interpretable system-bath models with combined Brownian and Drude spectral distribution functions, enabling rigorous quantum simulations of ultrafast vibrational energy relaxation and dephasing in solution via the hierarchical equations of motion (HEOM).

Kwanghee Park, Ju-Yeon Jo, Yoshitaka Tanimura2026-03-18🔬 physics

Potential-Barrier Affinity Effect in Solid Systems

This paper introduces the potential-barrier affinity (PBA) effect, a quantum phenomenon where electrons accumulate in interatomic regions when their energy exceeds the potential barrier, thereby overturning traditional views on electride localization and establishing a new fundamental mechanism for chemical bonding and material design.

Qiang Xu, Zhao Liu, Yanming Ma2026-03-18🔬 cond-mat.mtrl-sci

Toward Quantum-Aware Machine Learning: Improved Prediction of Quantum Dissipative Dynamics via Complex Valued Neural Networks

This paper introduces complex-valued neural networks (CVNNs) as a physics-consistent framework that outperforms traditional real-valued models in predicting quantum dissipative dynamics by preserving essential amplitude-phase correlations, thereby achieving superior convergence, stability, and physical fidelity for open quantum systems.

Muhammad Atif, Arif Ullah, Ming Yang2026-03-18🔬 physics

HEOM-Based Numerical Framework for Quantum Simulation of Two-Dimensional Vibrational Spectra in Molecular Liquids (HEOM-2DVS)

This paper introduces HEOM-2DVS, a high-performance computational framework based on hierarchical equations of motion for simulating non-Markovian, anharmonic vibrational dynamics in molecular liquids, which is validated through the calculation of two-dimensional infrared spectra for coupled water modes.

Ryotaro Hoshino, Yoshitaka Tanimura2026-03-18🔬 physics

V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction

This paper introduces V2Rho-FNO, a universal framework leveraging Fourier Neural Operators to directly map external potentials to electron densities, achieving accurate, zero-shot generalization across diverse and unseen molecular systems without relying on explicit atomic orbitals or handcrafted descriptors.

Yingdi Jin, Xinming Qin, Ruichen Liu, Jie Liu, Zhenyu Li, Jinlong Yang2026-03-18🔬 physics

Life cycle assessment for all organic chemicals

The paper introduces the CRYSTAL framework, which leverages retrosynthesis and machine learning to automatically generate transparent Life Cycle Inventory data for over 70,000 organic chemicals, thereby creating a comprehensive database to identify environmental hotspots and guide sustainable interventions in the chemical industry.

Shaohan Chen, Tim Langhorst, Julian Nöhl, Christopher Oberschelp, Martin Pillich, Johannes Schilling, André Bardow2026-03-18🔬 physics

On the performance of QTP functionals applied to second-order response properties II: Dynamic polarizability and long-range C $_6$ coefficients

This study extends the evaluation of Quantum Theory Project (QTP) functionals to dynamic polarizabilities and long-range C $_6$ dispersion coefficients, identifying TPSS0 and QTP01 as the top performers for dynamic polarizabilities and QTP01 and LC-QTP as the best within the QTP family for C $_6$ coefficients among 25 tested exchange-correlation functionals.

Rodrigo A. Mendes, Peter R. Franke, Ajith Perera, Rodney J. Bartlett2026-03-18🔬 physics

Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion-Pairing

This paper introduces an embedded correlated wavefunction transfer learning (ECW-TL) framework that combines high-level quantum mechanical accuracy with machine learning efficiency to achieve chemically accurate simulations of complex aqueous processes, demonstrated by successfully modeling calcium carbonate ion-pairing in seawater.

Xuezhi Bian, Emily A. Carter2026-03-18🔬 physics

Velocity Gauge for Oscillator Strength in $\Delta$ SCF theory

This paper proposes using the velocity gauge within $\Delta$ SCF theory to compute oscillator strengths, demonstrating that this approach naturally resolves the non-orthogonality and origin-dependence issues of standard methods to yield accurate, origin-independent results for conjugated chromophores, especially when combined with spin-purified singlet excitation energies.

Yang Shen, Yichen Fan, Weitao Yang2026-03-18🔬 physics

PFP/MM: A Hybrid Approach Combining a Universal Neural Network Potential with Classical Force Fields for Large-Scale Reactive Simulations

The paper introduces PFP/MM, a hybrid method combining universal neural network potentials with classical force fields to enable efficient, large-scale reactive molecular simulations of complex condensed-phase systems with near-DFT accuracy.

Yu Miyazaki, Atsuhiro Tomita, Akihide Hayashi, So Takemoto, Mizuki Takemoto, Hodaka Mori2026-03-18🔬 cond-mat.mtrl-sci

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