化学物理
Interfaces play a crucial role in energy transport at the nanoscale. However, direct experimental observations of interfacial thermal conductance across molecular junctions have remained challenging due to the high spatiotemporal resolution…
Although squaraine dyes are commonly praised as candidates for light-based applications, little is known about their excited state landscape beyond the low-energy visible light region. Our work aims for an improved understanding of the…
This study explores the use of equivariant quantum neural networks (QNN) for generating molecular force fields, focusing on the rMD17 dataset. We consider a QNN architecture based on previous research and point out shortcomings in the…
We present high-quality reference data for two fundamentally important groups of molecular properties related to a compound's utility as a lithium battery electrolyte. The first property is energy changes associated with charge excitations…
We present an ab initio method for computing vibro-polariton and phonon-polariton spectra of molecules and solids coupled to the photon modes of optical cavities. We demonstrate that if interactions of cavity photon modes with both nuclear…
An accurate determination of singlet-triplet gaps in biradicals, including cyclobutadiene in the automerization barrier region where one has to balance the substantial nondynamical many-electron correlation effects characterizing the…
We introduce an electron-photon exchange-correlation functional for quantum electrodynamical density-functional theory (QEDFT). The approach, photon MBD (pMBD), is inspired by the many-body dispersion (MBD) method for weak intermolecular…
The coherent-state initial-value representation (IVR) for the semi-classical real-time propagator of a quantum system, developed by Herman and Kluk (HK), is widely used in computational studies of chemical dynamics. On the other hand, the…
Machine-learning force fields (MLFFs) have emerged as a promising solution for speeding up ab initio molecular dynamics (MD) simulations, where accurate force predictions are critical but often computationally expensive. In this work, we…
Equivariant atomistic machine learning models have brought substantial gains in both extrapolation capability and predictive accuracy. Depending on the basis of the space, two distinct types of irreducible representations are utilized. From…
Molecular crystals possess a highly complex crystallographic landscape which in many cases results in the experimental observation of multiple crystal structures for the same compound. Accurate results can often be obtained for such systems…
Coupled electronic and nuclear motions govern chemical reactions, yet disentangling their interplay during bond rupture remains challenging. Here we follow the light-induced fragmentation of Br$_2$ using a coincidence-based multi-messenger…
The inhomogeneous distribution of P1 centers in type 1b HPHT diamond samples allows multiple DNP mechanisms to occur within the same crystal, resulting in complex DNP spectra. At some crystal orientations, different DNP mechanisms can…
We develop and employ general Tree Tensor Networks (TTNs) to compute the vibrational spectra for two model systems: a set of 64-dimensional coupled oscillators and acetonitrile. We explore various tree architectures, ranging from the simple…
The present study presents a comprehensive theoretical investigation of atom and asymmetric top molecule inelastic scattering based on the R-matrix formalism. The proposed methodology establishes a rigorous framework for treating inelastic…
Modeling plasmonic catalysis by applying femtosecond laser pulses of high intensity ($10^{13}-10^{15}$ W cm$^{-2}$), although justified by the time-dependent density functional theory (TDDFT) time-scale limitations, can lead to a…
Accurately and efficiently describing strongly correlated electronic systems is a central challenge in quantum computational chemistry, with classical and quantum computers. The localized active space self-consistent field method (LASSCF)…
Multiply charged actinide molecules provide a unique platform to study fundamental physics and the chemical bond under extreme conditions. Beyond the inherently large relativistic effects associated with a high proton number $Z$, an…
Plasmonic catalysis is a rapidly growing field of research, both from experimental and computational perspectives. Experimental observations demonstrate an enhanced dissociation rate for molecules in the presence of plasmonic nanoparticles…
Iontronics is a burgeoning paradigm that employs ions in solution as information carriers for sensing and computing, e.g., in neuromorphic devices. The fundamentally different working principle as compared to electronics requires novel…