Chemical Physics
Vibronic coupling plays a crucial role in X-ray photoelectron spectra (XPS) of molecules. In a series of three papers, we present a comprehensive exploration of the N-heterocycles family, known for their diverse structures, to summarize the…
For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Correspondingly, and in order to reduce cost and carbon…
The vibronic coupling effect in nitrogen 1s X-ray photoelectron spectra (XPS) was systematically studied for a family of 17 bicyclic indole molecules by combining Franck-Condon simulations (including the Duschinsky rotation effect) and…
Optical spectroscopy of molecular radicals is an important tool in physical chemistry, and is a prerequisite for many experiments which use molecules for quantum science and precision measurement. However, even the simplest molecules have…
Pump-push-probe spectroscopy was employed for the exploration of charge-separation processes in organic photovoltaic blends as well as for proton-coupled electron-transfer (PCET) reactions in hydrogen-bonded complexes of…
Poly(vinyl alcohol)/Poly(vinylidene fluoride) blends at mass ratio 3:1 with Nano - Graphene Platelet fillers constitute mechanically and thermally stable systems, which are used for developing piezoelectric devices. Blends host a fraction…
This study presents an experimental determination of spectroscopic parameters for the less-abundant isotopologues $^{42}$CaF and $^{44}$CaF, alongside $^{40}$CaF, using high-resolution laser-induced fluorescence spectroscopy in a skimmed…
Excited electronic states of fenchone, thiofenchone, and selenofenchone are characterized and assigned with different gas-phase spectroscopic methods and \textit{ab initio} quantum chemical calculations. With an increasing atomic number of…
Density functional theory (DFT) offers a desirable balance between quantitative accuracy and computational efficiency in practical many-electron calculations. Its central component, the exchange-correlation energy functional, has been…
Quantum mechanical equations of motion are strictly linear in state descriptors, such as wavefunctions and density matrices, but equations describing chemical kinetics and hydrodynamics may be non-linear in concentrations. This…
All-atom molecular dynamics (MD) computer simulations are a valuable tool for characterizing the conformational ensembles of intrinsically disordered proteins (IDPs). IDP conformational ensembles are highly heterogeneous and contain…
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications.…
Machine learning plays an increasingly important role in computational chemistry and materials science, complementing computationally intensive ab initio and first-principles methods. Despite their utility, machine-learning models often…
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale,…
The Simulation Environment for Atomistic and Molecular Modeling (SEAMM) is an open-source software package written in Python that provides a graphical interface for setting up, executing, and analyzing molecular and materials simulations.…
Catalysis lies at the heart of chemical reactivity, yet its foundational principles remain fragmented across the distinct domains of homogeneous, heterogeneous, and enzymatic systems Here, we propose a unifying theoretical model that…
Since its inception, the ReSpect program has been evolving to provide powerful tools for simulating spectroscopic processes and exploring emerging research areas, all while incorporating relativistic effects, particularly spin-orbit…
Unitary coupled cluster (UCC) theory offers a promising Hermitian alternative to conventional coupled cluster (CC) theory, but its practical implementation is hindered by the non-truncating nature of the Baker-Campbell-Hausdorff (BCH)…
The calculation of reactive properties is a challenging task in chemical reaction discovery. Machine learning (ML) methods play an important role in accelerating electronic structure predictions of activation energies and reaction…
Unconditional molecular generation is a stepping stone for conditional molecular generation, which is important in \emph{de novo} drug design. Recent unconditional 3D molecular generation methods report saturated benchmarks, suggesting it…