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A transferable force field for gallium nitride crystal growth from the melt u...
Atomic-scale simulations of reactive processes have been stymied by two factors: the general lack of a suitable semi-empirical force field on the one hand, and the impractically... -
Graph theory-based structural analysis on density anomaly of silica glass
Understanding the structure of glassy materials represents a tremendous challenge for both experiments and computations. Despite decades of scientific research, for instance,... -
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... -
Deep learning the slow modes for rare events sampling
The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational... -
Learning local equivariant representations for large-scale atomistic dynamics
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural... -
Confinement effects and acid strength in Zeolites
Chemical reactivity and sorption in zeolites are coupled to confinement and - to a lesser extent- to the acid strength of Brønsted acid sites (BAS). In presence of water the... -
Electron and Hole Polarons at the BiVO4–Water Interface
We determine the transition levels of electron and hole polarons at the BiVO4–water interface through thermodynamic integration within a hybrid functional scheme, thereby... -
Multi-technique approach to unravel the (dis)order in amorphous materials
The concept of order in disordered materials is the key to controlling the mechanical, electrical, and chemical properties of amorphous compounds widely exploited in industrial... -
Impact of glutamate carboxylation in the adsorption of the alpha-1 domain of ...
One proposed mechanism of implant fouling is attributed to the nonspecific adsorption of non-collagenous bone matrix proteins (NCPs) onto a newly implanted interface. With the... -
Unsupervised landmark analysis for jump detection in molecular dynamics simul...
Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states... -
Fast Bayesian force fields from active learning: study of inter-dimensional t...
Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor... -
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... -
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomist...
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training... -
Data and scripts for paper "Modelling Membrane Reshaping by Staged Polymeriza...
Data and scripts for paper "Modelling Membrane Reshaping by Staged Polymerization of ESCRT-III Filaments". Including data points for figures in main and SI, and LAMMPS input file. -
BELLO: A post-processing tool for the local-order analysis of disordered systems
The characterization of the atomic structure of disordered systems, such as amorphous, glasses and (bio)molecule in solution, is a fundamental step for most theoretical... -
Differentiable sampling of molecular geometries with uncertainty-based advers...
Neural network (NN) force fields can predict potential energy surfaces with high accuracy and speed compared to electronic structure methods typically used to generate their... -
Assessing the persistence of chalcogen bonds in solution with neural network ...
Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet,... -
The solid-state Li-ion conductor Li7TaO6: A combined computational and experi...
We study the oxo-hexametallate Li7TaO6 with first-principles and classical molecular dynamics simulations, obtaining a low activation barrier for diffusion of ∼0.29 eV and a... -
The mapped gaussian process (MGP) force-field of Cu-Zn surface alloy
The mapped gaussian process (MGP) force-field used to elucidate the surface alloying of Cu-Zn. The force-field is made based on first-principles data by using machine-learning... -
Invariance principles in the theory and computation of transport coefficients
In this work we elaborate on recently discovered invariance principles, according to which transport coefficients are, to a large extent, independent of the microscopic...