Related papers: Permutationally Invariant, Reproducing Kernel-Base…
Non-adiabatic effects play an important role in many chemical processes. In order to study the underlying non-adiabatic potential-energy surfaces (PESs), we present a locally-constrained density-functional theory approach, which enables us…
Kernel models of potential energy surfaces (PES) for polyatomic molecules are often restricted by a specific choice of the kernel function. This can be avoided by optimizing the complexity of the kernel function. For regression problems…
Molecular adsorption at organic/metal interfaces depends on a range of mechanisms: covalent bonds, charge transfer, Pauli repulsion and van der Waals (vdW) interactions shape the potential energy surface (PES), making it key to…
The basic formal and numerical aspects of different degree interpolated moving least-squares (IMLS) methods are studied using sixteen different combinations of coordinate system for fitting and weight functions. For the application we use…
High-quality potential energy surfaces (PES) are a prerequisite for quantitative atomistic simulations, with both quantum and classical dynamics approaches. The ultimate test for the validity of a PES are comparisons with judiciously chosen…
Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally…
A typical task for classical and quantum computing in chemistry is finding a potential energy surface (PES) along a reaction coordinate, which involves solving the quantum chemistry problem for many points along the reaction path.…
A detailed characterization of the interaction between the most abundant molecules in air is important for the understanding of a variety of phenomena in atmospherical science. A completely {\em ab initio} global potential energy surface…
Quantum chemistry provides accurate and reliable methods to investigate reaction pathways of reactive molecular systems relevant to the interstellar medium. However, the exhaustive exploration of a reactive network is often a daunting task,…
Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via…
We present the first fully ab initio calculations of collision-induced broadening and shift of spectral lines in antiprotonic helium ($\bar{p}$He$^{+}$) perturbed by atomic helium. To overcome critical limitations of previous studies, we…
The two most recently published potential energy surfaces (PESs) for the HeH$_2$ complex, the so-called MR (Muchnick and Russek) and BMP (Boothroyd, Martin, and Peterson) surfaces, are quantitatively evaluated and compared through the…
Despite the fundamental nature of the inhomogeneous Poisson process in the theory and application of stochastic processes, and its attractive generalizations (e.g. Cox process), few tractable nonparametric modeling approaches of intensity…
The predictive simulation of molecular liquids requires models that are not only accurate, but computationally efficient enough to handle the large systems and long time scales required for reliable prediction of macroscopic properties. We…
Motivated by the growing interest in representation learning approaches that uncover the latent structure of high-dimensional data, this work proposes new algorithms for reconstruction-based manifold learning within Reproducing-Kernel…
Monitoring ozone concentrations in the Earth's atmosphere using spectroscopic methods is a major activity which undertaken both from the ground and from space. However there are long-running issues of consistency between measurements made…
For the prototypical diatomic-molecule - diatomic molecule interactions H2-HX and H2-X2, where X = F, Cl, Br, quantum-chemical ab initio calculations are carried out on grids of the configuration space, which permit a spherical-harmonics…
Accurate energy demand forecasting is crucial for sustainable and resilient energy development. To meet the Net Zero Representative Concentration Pathways (RCP) $4.5$ scenario in the DACH countries, increased renewable energy production,…
Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions.…
We introduce a compact yet highly expressive design space for shellular metamaterials. By employing only a few dozen degrees of freedom, this design space represents geometries ranging from simple planar configurations to complex triply…