-
Bias free multiobjective active learning for materials design and discovery
The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where... -
Active learning of reactive Bayesian force fields applied to heterogeneous ca...
Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we... -
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... -
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... -
Data-driven studies of magnetic two-dimensional materials
We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on... -
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... -
Fast Bayesian force fields from active learning and mapped Gaussian processes...
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... -
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
Here we present 1,000 structures each of a water monomer, water dimer, Zundel cation and bulk water used to train tensorial machine-learning models in Phys. Rev. Lett. 120,... -
Unified theory of atom-centered representations and message-passing machine-l...
Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of... -
The QMspin data set: Several thousand carbene singlet and triplet state struc...
High-quality data sets of free carbenes have remained unavailable in the scientific literature so far. We provide approximately 5k and 8k verified carbene structures in their... -
Properties of α-brass nanoparticles. 1. Neural network potential energy surface
Data for Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface Jan Weinreich, Anton Römer, Martín Leandro Paleico, and Jörg Behler 53 841 reference... -
E(3)-equivariant graph neural networks for data-efficient and accurate intera...
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio... -
Finite-temperature materials modeling from the quantum nuclei to the hot elec...
Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be... -
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... -
ozone-imputation-data
Data of the ozone imputation software (https://gitlab.jsc.fz-juelich.de/esde/machine-learning/ozone-imputation), see README of this repository for usage and information.... -
QMrxn20: Thousands of reactants and transition states for competing E2 and SN...
For competing E2 and SN2 reactions, we report 4'400 validated transition state geometries and 143'200 reactant complex geometries including conformers obtained at MP2/6-311G(d)... -
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... -
Ligand optimization of exchange interaction in Co(II) dimer single molecule m...
This record contains data structures used in the manuscript titled Ligand optimization of exchange interaction in Co(II) dimer single molecule magnet by machine learning.... -
Sensitivity benchmarks of structural representations for atomic-scale machine...
This dataset contains three sets of CH4 geometries that are distorted along special directions, to reveal the sensitivity to atomic displacements of structural descriptors used...