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Effect of hydrogen on the local chemical bonding states and structure of amor...
This study discloses the effect of hydrogen impurities on the local chemical bonding states and structure of amorphous alumina films by predicting measured Auger parameter... -
Uncovering the origin of interface stress enhancement and compressive-to-tens...
The intrinsic stress in nanomultilayers (NMLs) is typically dominated by interface stress, which is particularly high in immiscible Cu/W NMLs. Here, atomistic simulations with a... -
Effect of residual stress and microstructure on mechanical properties of sput...
The combination of the high wear resistance and mechanical strength of W with the high thermal conductivity of Cu makes the Cu/W system an attractive candidate material for heat... -
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... -
Machine learning of twin/matrix interfaces from local stress field
Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic... -
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... -
Chemical shift-dependent interaction maps in molecular solids
Structure determination of molecular solids through NMR crystallography relies on the generation of a comprehensive set of candidate crystal structures and on the comparison of... -
Atomic-level structure determination of amorphous molecular solids by NMR
Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is... -
Do we really need machine learning interatomic potentials for modeling amorph...
In this study, we benchmarked various interatomic potentials and force fields in comparison to an ab initio dataset for bulk amorphous alumina. We investigated a comprehensive... -
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... -
Ab-initio phase diagram and nucleation of gallium
Elemental gallium possesses several intriguing properties such as a low melting point, a density anomaly and an electronic structure in which covalent and metallic features... -
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... -
The role of water in host-guest interaction
One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field... -
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... -
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... -
Structure determination of an amorphous drug through large-scale NMR predictions
Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous... -
Learning the energy curvature versus particle number in approximate density f...
The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions... -
Quantum mechanical dipole moments in the QM7b, 21k molecules of QM9, and MuML...
Molecular dipole moments of the QM7b dataset, a random sample of 21'000 molecules from the QM9 dataset, and the MuML showcase set (including the four challenge series) described... -
Local kernel regression and neural network approaches to the conformational l...
The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of... -
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...
