Related papers: Decoding Energy Decomposition Analysis: Machine-Le…
Machine learning techniques are utilized to estimate the electronic band gap energy and forecast the band gap category of materials based on experimentally quantifiable properties. The determination of band gap energy is critical for…
Although the precise microscopic knowledge of van der Waals interactions is crucial for understanding bonding in weakly bonded layered compounds, very little quantitative information on the strength of interlayer interaction in these…
The precise nature of chemical-bonding interactions in amorphous, and crystalline, chalcogenides is still unclear due to the complexity arising from the delocalization of bonding, and non-bonding, electrons. Although an increasing degree of…
The accurate determination of the lowest electron attachment ($EA$) and ionization ($IP$) energies for molecules embedded in molecular junctions is important for correctly estimating, \emph{e.g.}, the magnitude of the currents ($I$) or the…
The goal of this paper is to present a theoretical and practical introduction to generalized eigendecomposition (GED), which is a robust and flexible framework used for dimension reduction and source separation in multichannel signal…
The affinity and specificity of protein-molecule binding directly impact functional outcomes, uncovering the mechanisms underlying biological regulation and signal transduction. Most deep-learning-based prediction approaches focus on…
Learning about density functional approximations (DFAs), or approximations for the exchange-correlation functional, can be intimidating. Density Functional Theory is now one of the primary simulation tools for the practicing chemist or…
The effect of external static charging on borophenes - 2D boron crystals - is investigated by using first principles calculations. The influence of the excess negative charge on the stability of the 2D structures is examined using a very…
Electron charge density is a fundamental physical quantity, determining various properties of matter. In this study, we have proposed a deep-learning model for accurate charge density prediction. Our model naturally preserves physical…
Electroencephalography (EEG) signals are frequently used for various Brain-Computer Interface (BCI) tasks. While Deep Learning (DL) techniques have shown promising results, they are hindered by the substantial data requirements. By…
Theoretical studies of photochemical processes require a description of the energy surfaces of excited electronic states, especially near degeneracies, where transitions between states are most likely. Systems relevant to photochemical…
Strong ($10^{10}$ V/m) electric fields capable of inducing atomic bond-breaking represent a powerful tool for surface chemistry. However, their exact effects are difficult to predict due to a lack of suitable tools to probe their associated…
Using a plane wave pseudopotential approach to density functional theory we investigate the electron localization length in various oxides. For this purpose, we first set up a theory of the band-by-band decomposition of this quantity, more…
An observed broad resonance peak in dissociative electron attachment (DEA) cross sections to a molecule might be due to either closely lying or overlapping resonances involved in the process. We developed a state-of-the-art experimental…
The electronic properties of two-dimensional (2D) materials depend sensitively on the underlying atomic arrangement down to the monolayer level. Here we present a novel strategy for the determination of the band gap and complex dielectric…
Conceiving a molecule as composed of smaller molecular fragments, or subunits, is one of the pillars of the chemical and physical sciences, and leads to productive methods in quantum chemistry. Using a fragmentation scheme, efficient…
Two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs) can withstand a large deformation without fracture or inelastic relaxation, making them attractive for application in novel strain-engineered and flexible…
Density-functional theory is a formally exact description of a many-body quantum system in terms of its density; in practice, however, approximations to the universal density functional are required. In this work, a model based on deep…
What is the best reference quantum chemical approach to decipher the energycomponents of the total interaction energy : Symmetry-Adapted Perturbation Theory(SAPT) or Supermolecular Energy Decomposition Analysis (EDA) methods? With the rise…
Understanding the structure of complex, nonstationary, high-dimensional time-evolving signals is a central challenge in scientific data analysis. In many domains, such as speech and biomedical signal processing, the ability to learn…