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Anomalously low vacancy formation energies and migration barriers at Cu/AlN i...
It is well known that interfaces in nanomaterials can act as ultra-fast short-circuit diffusion paths, as originating from local structural, chemical and/or electronic... -
Incorporating long-range physics in atomic-scale machine learning
The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a... -
Electron density learning of non-covalent systems
Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the... -
Learning the exciton properties of azo-dyes
The ab initio determination of the character and properties of electronic excited states (ES) is the cornerstone of modern theoretical photochemistry. Yet, traditional ES... -
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... -
Experimental and ab initio derivation of interface stress in nanomultilayered...
Interface stress is a fundamental descriptor for interphase boundaries and is defined in strict relation to the interface energy. In nanomultilayered coatings with their... -
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... -
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... -
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... -
Rare-earth magnetic nitride perovskites
We propose perovskite nitrides with magnetic rare-earth metals as novel materials with a range of technological applications. These materials appear to be thermodynamically... -
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... -
Calculation of phase diagrams in the multithermal-multibaric ensemble
From the Ising model and the Lennard-Jones fluid to water and the iron-carbon system, phase diagrams are an indispensable tool to understand phase equilibria. Despite the effort... -
Data-Driven Collective Variables for Enhanced Sampling
Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from... -
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... -
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...
