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.

Efficient Coupled-Cluster Python Frameworks for Next-Generation GPUs: A Comparative Study of CuPy and PyTorch on the Hopper and Grace Hopper Architecture

This paper presents new batching algorithms and a generic tensor contraction protocol for coupled-cluster singles and doubles (CCSD) calculations on NVIDIA Hopper and Grace Hopper GPUs, demonstrating that optimized implementations using CuPy and PyTorch achieve up to a 16-fold speedup over previous hybrid CPU-GPU approaches, with PyTorch showing a 20% performance advantage on H100 while both libraries perform similarly on GH200.

Antonina Dobrowolska, Julian Swierczynski, Paweł Tecmer, Emil Sujkowski, Somayeh Ahmadkhani, Grzegorz Mazur, Klemens Noga, Jeff Hammond, Katharina Boguslawski2026-03-24🔬 physics

Measurement Reduction in Orbital-Optimized Variational Quantum Eigensolver via Orbital Compression

This paper introduces an orbital-optimized VQE framework utilizing frozen natural and split virtual orbitals to compress active spaces, thereby significantly reducing measurement costs while improving the accuracy of electronic structure calculations for molecular dissociation and reaction activation energies.

Yanxian Tao, Lingyun Wan, Jie Liu2026-03-24🔬 physics

Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding

This paper introduces a machine-learning-driven transition state optimizer that predicts the leftmost Hessian eigenvector to achieve robust, second-order convergence at first-order computational cost, significantly outperforming standard methods in efficiency and reliability for high-throughput reaction discovery.

Guanchen Wu, Chung-Yueh Yuan, Kareem Hegazy, Samuel M. Blau, Teresa Head-Gordon2026-03-24🔬 physics

Deformed states in paraelectric and ferroelectric nematic liquid crystals

This review examines how molecular shape, chirality, polarity, and spatial confinement induce deformed equilibrium and polydomain states with parity-breaking deformations in paraelectric and ferroelectric nematic liquid crystals, highlighting mechanisms such as splay cancellation and the interplay between elastic and electrostatic energies.

Oleg D. Lavrentovich2026-03-24🔬 cond-mat

TERS-ABNet: A Deep Learning Approach for Automated Single-Molecule Structure Reconstruction with Atomic Precision from TERS Mapping

This paper introduces TERS-ABNet, a deep learning framework that automates the reconstruction of single-molecule atomic structures from Tip-Enhanced Raman Spectroscopy (TERS) maps by formulating the task as an image-to-graph inference problem, achieving high accuracy in predicting atom types and coordinates and successfully demonstrating its capability on experimental porphyrin data.

Jie Cui, Yao Zhang, Yang Zhang, Yi Luo, Zhen-Chao Dong2026-03-24🔬 physics

Emergent single-species non-reciprocity from bistable chemical dynamics

This paper proposes a mechanism where bistable chemical dynamics within single-species, enzyme-encapsulated vesicles enable them to spontaneously and reversibly switch between acting as chemical producers or consumers, thereby generating emergent non-reciprocal interactions that drive rich collective behaviors like polar swarming.

Jakob Metson, Ramin Golestanian2026-03-24🔬 cond-mat

olLOSC: Unified and efficient density functional approximation to correct delocalization error in molecules and periodic materials

The paper introduces olLOSC, a unified and computationally efficient orbital-free density functional approximation that corrects delocalization errors in both molecules and periodic materials by calculating curvature via orbital-free electronic linear response, thereby enabling robust predictions of total energy, charge density, and band structure without the high cost of existing methods.

Yichen Fan, Jacob Z. Williams, Weitao Yang2026-03-24🔬 cond-mat.mtrl-sci

Molecular dynamics simulation of high slip flow of water confined between graphene nanochannels at experimentally accessible strain rates

This study demonstrates that the transient time correlation function (TTCF) method successfully enables the simulation of water slip flow in graphene nanochannels at experimentally accessible shear rates, yielding results consistent with equilibrium simulations and experiments where classical nonequilibrium molecular dynamics fails.

Carmelo Civello, Luca Maffioli, Edward Smith, James Ewen, Peter Daivis, Daniele Dini, Billy Todd2026-03-24🔬 cond-mat.mtrl-sci

Suiren-1.0 Technical Report: A Family of Molecular Foundation Models

The paper introduces Suiren-1.0, a family of open-source molecular foundation models that utilize spatial self-supervised pre-training and conformation compression distillation to bridge 3D geometry and 2D representations, achieving state-of-the-art performance in quantum property prediction and diverse organic system modeling.

Junyi An, Xinyu Lu, Yun-Fei Shi, Li-Cheng Xu, Nannan Zhang, Chao Qu, Yuan Qi, Fenglei Cao2026-03-24🔬 physics

Overcoming sampling limitations using machine-learned interatomic potentials: the case of water-in-salt electrolytes

This study demonstrates that machine-learned interatomic potentials, particularly through fine-tuned foundation models, effectively overcome the sampling limitations of ab initio methods to accurately model highly concentrated water-in-salt electrolytes over long timescales, while also highlighting the critical impact of reference functional choices on dispersion corrections.

Luca Brugnoli, Mathieu Salanne, A. Marco Saitta, Alessandra Serva, Arthur France-Lanord2026-03-24🔬 physics

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