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.

Interface-dependent Phase Transitions and Ultrafast Hydrogen Superionic Diffusion of H2O Ice

By integrating artificial neural networks with large-scale molecular dynamics simulations, this study demonstrates that the diamond-ice interface significantly alters high-pressure water behavior by lowering the superionic transition temperature, inducing spontaneous bcc-to-fcc phase transitions via the inverse Bain mechanism, and redefining the stability fields of ice phases, thereby resolving discrepancies between theoretical predictions and experimental observations.

Pengfei Hou, Yumiao Tian, Zifeng Liu, Junwen Duan, Hanyu Liu, Xing Meng, Russell J. Hemley, Yanming Ma2026-03-19🔬 cond-mat.mtrl-sci

TENSO: Software Package for Numerically Exact Open Quantum Dynamics Based on Efficient Tree Tensor Network Decomposition of the Hierarchical Equations of Motion

TENSO is a versatile, open-source software package that enables numerically exact simulations of non-Markovian open quantum dynamics in complex thermal environments by utilizing efficient tree tensor network decompositions of the hierarchical equations of motion to overcome dimensionality challenges.

Juan C. Rodriguez Betancourt, Michelle C. Anderson, Luchang Niu, Xinxian Chen, Ignacio Franco2026-03-19🔬 physics.optics

The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery

This paper proposes that integrating High-Performance Computing, Machine Learning, and High-Performance Quantum Computing into a unified framework will overcome the historical trade-off between chemical accuracy and computational scalability, thereby revolutionizing next-generation drug discovery and materials science.

Narjes Ansari, César Feniou, Nicolaï Gouraud, Daniele Loco, Siwar Badreddine, Baptiste Claudon, Félix Aviat, Marharyta Blazhynska, Kevin Gasperich, Guillaume Michel, Diata Traore, Corentin Villo (…)2026-03-19⚛️ quant-ph

Mechanistic Insights into Enhanced Alkaline Oxygen Evolution on Zn-Al Alloy Electrodes

This study demonstrates that Zn-Al alloy electrodes with 10–15 wt.% aluminum content significantly enhance alkaline oxygen evolution reaction performance by optimizing thermodynamic stability and electronic structure, achieving superior catalytic activity and lower overpotentials compared to pure zinc and other transition-metal-based catalysts.

Abdul Ahad Mamun, Rokon Uddin Mahmud, Shahin Aziz, Muhammad Shahriar Bashar, Ahmed Sharif, Muhammad Anisuzzaman Talukder2026-03-19🔬 cond-mat.mtrl-sci

Rotational excitation of asymmetric-top molecular ions by electron impact: application to H2_2O+^+, HDO+^+, and D2_2O+^+

This paper theoretically investigates the rotational excitation of asymmetric-top molecular ion isotopologues H2_2O+^+, HDO+^+, and D2_2O+^+ by electron impact using a combined framework of R-matrix scattering, multichannel quantum-defect theory, and adapted frame transformation and Coulomb-Born approximations to provide state-resolved cross sections and kinetic rate coefficients.

Joshua Forer2026-03-19🔬 physics.atom-ph

Quantum Chemistry Driven Molecular Inverse Design with Data-free Reinforcement Learning

This paper presents a data-free reinforcement learning framework that integrates quantum mechanics calculations to generate novel molecules with desired properties, achieving significant speed-ups and success in both known and unexplored chemical spaces without relying on pre-trained datasets.

Francesco Calcagno, Luca Serfilippi, Giorgio Franceschelli, Marco Garavelli, Mirco Musolesi, Ivan Rivalta2026-03-18🔬 physics