化学物理

Metal coordination is ubiquitous in Nature and central in many applications ranging from nanotechnology to catalysis and environmental chemistry. Complex formation results from the subtle interplay between different thermodynamic, kinetic,…

In this work, we present the analytical derivation and implementation of the quantum electrodynamics Hartree-Fock Hessian. We investigate how electronic strong coupling influences molecular vibrational properties, applying this framework to…

We study the isomer-specific photoionization and photofragmentation of 1,2- dibromoethene (DBE) under strong-field fs-laser irradiation in the gas phase complementing previous studies utilising ns- and ps-laser excitation. Our findings are…

This study presents a novel approach for the degradation of tetracycline (TC) in pig manure wastewater using magnet-ite-based magnetic nanofibers (MNFs) as heterogeneous Fenton-like catalysts. The MNFs, composed of polyacrylonitrile (PAN)…

We apply the physics-learning duality to molecular systems by complementing the physical description of interacting particles with a dual learning description, where each particle is modeled as an agent minimizing a loss function. In the…

We present a size-invariant (i.e., $N^0$) scaling algorithm for simulating fluorescence spectroscopy in large molecular aggregates. We combine the dyadic adaptive hierarchy of pure states (DadHOPS) equation-of-motion with an operator…

Exchange-correlation potentials vxc and energy densities exc are derived for integer and fractional electron counts using an orbital-averaged Kohn-Sham inversion procedure. The reference densities for inversion come from full configuration…

Thin-wall metal ultramicro- and nanoelectrodes (UMEs/NEs), especially gold NEs, are indispensable for high-resolution electrochemical microscopy, biosensing, and fundamental research. However, their damage susceptibility and the lack of…

The development of relativistic exact two-component (X2C) theory is briefly reviewed, with an emphasis on cost-effective treatments of relativistic two-electron contributions by means of model potential (MP) techniques and closely related…

In this mini-review I look into the physics underlying the theory of electronic structure of atoms and molecules. Quantum mechanics is needed to understand the structure of the periodic table. Special relativity is indispensable for a…

Machine learning has emerged as a powerful tool for predicting molecular properties in chemical reaction networks with reduced computational cost. However, accurately predicting energies of transition state (TS) structures remains a…

In order to establish the thermodynamic stability of a system, knowledge of its Gibbs free energy is essential. Most often, the Gibbs free energy is predicted within the CALPHAD framework using models employing thermodynamic properties,…

We conducted a first-principles study of the dielectric properties of liquid propylene glycol (PG) and polypropylene glycol (PPG) using a recently developed chemical bond-based machine learning (ML) model for dipole moments [T. Amano et al.…

Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in deep learning, existing models cannot generate molecules that satisfy all desired physicochemical properties. Herein, we describe IDOLpro, a…

This work presents the automatic generation of analytic first derivatives of the energy for general coupled-cluster models using the \text{tenpi} toolchain. We report the first implementation of expectation values for CCSDT and CCSDTQ…

Chemical synthesis, as a foundational methodology in the creation of transformative molecules, exerts substantial influence across diverse sectors from life sciences to materials and energy. Current chemical synthesis practices emphasize…

The problem of calculating the electron-positively charged particle correlation energy poses a challenge in the field of quantum chemistry beyond the adiabatic approximation. In this study, a toy model called Exotic Harmonium is developed…

This work presents new Gaussian single- and double-zeta basis sets optimized for stochastic density functional theory (sDFT) using real-space auxiliary grids. Previous studies showed standard basis sets like STO-3G and 6-31G are sub-optimal…

Accurately modeling open quantum system dynamics is crucial for advancing quantum technologies, yet traditional methods struggle to balance accuracy and efficiency. Machine learning (ML) provides a promising alternative, particularly…

This note responds to Schwartz and Hutchison's Comment (arXiv:2403.06001) on our article (DOI:10.1002/adma.202309393). We think differences have arisen not in the experimental results themselves but in their interpretation: our more…