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Learning on-top: regressing the on-top pair density for real-space visualizat...
The on-top pair density [Π(r)] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space. Simplest... -
Structure-property maps with kernel principal covariates regression
Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building... -
Bayesian probabilistic assignment of chemical shifts in organic solids
A pre-requisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment... -
Influence of an external electric field on the potential energy surface of al...
We present a fully ab-initio, unbiased structure search of the configurational space of decorated C60 fullerenes in the presence of an electric field. We observed that the... -
Divalent Path to Enhance p-Type Conductivity in a SnO Transparent Semiconductor
The role of the divalent nature of tin is explored in tin monoxide, revealing a novel path for enhancing p-type conductivity. The consequences of oxygen off-stoichiometry... -
A Cannibalistic Approach to Grand Canonical Crystal Growth
Canonical molecular dynamics simulations of crystal growth from solution suffer from severe finite-size effects. As the crystal grows, the solute molecules are drawn from the... -
Two-dimensional pure isotropic proton solid state NMR
One key bottleneck of solid-state NMR spectroscopy is that ¹H NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We... -
Solvent-mediated morphology selection of the active pharmaceutical ingredient...
In solution crystallization, solvent has a profound effect on controlling crystal morphology. However, the role played by solvents in affecting crystal morphology remains... -
Rethinking Metadynamics
Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is a function of a selected number of collective... -
Evidence for carbon clusters present near thermal gate oxides affecting the e...
High power SiC MOSFET technologies are critical for energy saving in, e.g., distribution of electrical power. They suffer, however, from low near-interface mobility, the origin... -
Surface reconstructions and premelting of the (100) CaF2 surface
In this work, surface reconstructions on the (100) surface of CaF2 are comprehensively investigated. The configurations were explored by employing the Minima Hopping Method... -
Stable structures of exohedrally decorated C60-fullerenes
A good hydrogen storage material should adsorb hydrogen in high concentrations and with optimal binding energies. Exohedrally metal decorated carbon fullerene structures were... -
Impact of quantum-chemical metrics on the machine learning prediction of elec...
Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain... -
Balancing DFT Interaction Energies in Charged Dimers Precursors to Organic Se...
Accurately describing intermolecular interactions within the framework of Kohn-Sham density functional theory (KS-DFT) has resulted in numerous benchmark databases over the past... -
A machine learning model of chemical shifts for chemically and structurally d...
Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the... -
Pure isotropic proton NMR spectra in solids using deep learning
The resolution of proton solid-state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic-angle spinning alleviates this... -
Improving collective variables: The case of crystallization
Several enhanced sampling methods, such as umbrella sampling or metadynamics, rely on the identification of an appropriate set of collective variables. Recently two methods have... -
Neural networks-based variationally enhanced sampling
Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a... -
Temperature Dependence of Homogeneous Nucleation in Ice
Ice nucleation is a process of great relevance in physics, chemistry, technology, and environmental sciences; much theoretical effort has been devoted to its understanding, but... -
Chemical Shifts in Molecular Solids by Machine Learning Datasets
We present a database of energy and NMR chemical shifts DFT calculations of 4150 crystal organic solids. The structures contain only H/C/N/O/S atoms and were subject to...