Chemical Physics
We introduce a unified statistical framework for quantifying system-environment coupling by treating the interaction energy $V_\mathcal{SE}$ as a stochastic variable. Using a reference-particle decomposition, we derive exact, closed-form…
This article summarizes recent updates to the p$^\dagger$q package, which is a C++ accelerated Python library for generating equations and computer code corresponding to singly-reference many-body quantum chemistry methods such as…
Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning applied to chemistry, we…
Predicting the degradation processes of molecules over long timescales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are…
Eccentric mixing is a typical chaotic mixing method, and the study of its mixing characteristics is beneficial to the optimization of the mixing process. In this study, the effects of eccentricity (E/R) and rotational speed (N) on the…
We employ a general spectral approach based on the Steklov eigenbasis to describe imperfect diffusion-controlled reactions on bounded reactive targets in three dimensions. The steady-state concentration and the total diffusive flux onto the…
In this paper, we numerically optimize broadband pulse shapes that maximize Hahn echo amplitudes. Pulses are parameterized as neural networks (NN), nonlinear amplitude limited Fourier series (FS), and discrete time series (DT). These are…
Understanding and controlling charge carrier recombination dynamics is essential for enhancing the performance of metal halide perovskite optoelectronic devices. In this study, we present a machine learning-assisted intensity-modulated…
In all standard force field-based simulations of organic, bio-molecules, polymers; the torsion angle based Hamiltonian has been an indispensable term to set up molecular simulation. Torsion often termed as a dihedral angle, the coordinate…
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…