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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... -
Zeo-1: A computational data set of zeolite structures
Fast, empirical potentials are gaining increased popularity in the computational fields of materials science, physics and chemistry. With it, there is a rising demand for... -
Training sets based on uncertainty estimates in the cluster-expansion method
Cluster expansion (CE) has gained an increasing level of popularity in recent years, and many strategies have been proposed for training and fitting the CE models to... -
Finding new crystalline compounds using chemical similarity
We proposed an efficient high-throughput scheme for the discovery of new stable crystalline phases. Our approach was based on the transmutation of known compounds, through the... -
Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate... -
The importance of nuclear quantum effects for NMR crystallography
The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of the computational prediction of NMR chemical shieldings of... -
Sampling enhancement by metadynamics driven by machine learning and de novo p...
Folding of villin miniprotein was studied by parallel tempering metadynamics driven by machine learning. To obtain a training set for machine learning, we generated a large... -
On-the-fly assessment of diffusion barriers of disordered transition metal ox...
The dataset contains the result of 48 Nudged Elastic Band calculations of Li(2-x)VO2F diffusion barriers in the format of Atomic Simulation Environment (ASE) trajectories. The... -
Modeling high-entropy transition-metal alloys with alchemical compression: da...
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar... -
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... -
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... -
Mining the C-C Cross-Coupling Genome using Machine Learning
Applications of machine-learning (ML) techniques to the study of catalytic processes have begun to appear in the literature with increasing frequency. The computational speed up... -
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... -
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,... -
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... -
Viscosity in water from first-principles and deep-neural-network simulations
We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio... -
On the robust extrapolation of high-dimensional machine learning potentials
We show that, contrary to popular assumptions, predictions from machine learning potentials built upon high-dimensional atom-density representations almost exclusively occur in... -
Simulating solvation and acidity in complex mixtures with first-principles ac...
Set of inputs to perform the calculations reported in the paper. The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate... -
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... -
Predicting hot-electron free energies from ground-state data
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation...