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Adaptive energy reference for machine-learning models of the electronic densi...
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and... -
Adaptive energy reference for machine-learning models of the electronic densi...
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and... -
Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-r...
Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or... -
DFT calculations of surface binding and interstitial hydrogen formation energ...
This dataset contains the results of density functional theory (DFT) calculations performed using Quantum ESPRESSO to study surface binding energies (SBE) and the formation... -
Adaptive energy reference for machine-learning models of the electronic densi...
The electronic density of states (DOS) provides information regarding the distribution of electronic states in a material, and can be used to approximate its optical and... -
Two-dimensional materials from high-throughput computational exfoliation of e...
Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have... -
Two-dimensional materials from high-throughput computational exfoliation of e...
Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have... -
Revising known concepts for novel applications: Fe incorporation into Ni-MOF-...
The performance of Ni-based oxygen evolution reaction (OER) electrocatalysts is enhanced upon Fe incorporation into the structure or Fe uptake from the electrolyte. In light of... -
Data-driven discovery of organic electronic materials enabled by hybrid top-d...
The high-throughput molecular exploration and screening of organic electronic materials often starts with either a 'top-down' mining of existing repositories, or the 'bottom-up'... -
Automated computational workflows for muon spin spectroscopy
Muon spin rotation and relaxation spectroscopy is a powerful tool for studying magnetic materials, offering a local probe that complements scattering techniques and provides... -
Automated prediction of ground state spin for transition metal complexes
Predicting the ground state spin of transition metal complexes is a challenging task. Previous attempts have been focused on specific regions of chemical space, whereas a more... -
Spectral operator representations
Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an alternative... -
Orbital-resolved DFT+U for molecules and solids
We present an orbital-resolved extension of the Hubbard U correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of... -
Machine learning potential for the Cu-W system
Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix... -
Automated prediction of ground state spin for transition metal complexes
Predicting the ground state spin of transition metal complexes is a challenging task. Previous attempts have been focused on specific regions of chemical space, whereas a more... -
Thermal transport of Li₃PS₄ solid electrolytes with ab initio accuracy
The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been... -
Density functional perturbation theory for one-dimensional systems: implement...
The electronic and vibrational properties and electron-phonon couplings of one-dimensional materials will be key to many prospective applications in nanotechnology.... -
A bridge between trust and control: Computational workflows meet automated ba...
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this... -
The energy landscape of magnetic materials
Magnetic materials can display many solutions to the electronic-structure problem, corresponding to different local or global minima of the energy functional. In Hartree-Fock or... -
Electronic decoupling and hole-doping of graphene nanoribbons on metal substr...
In this record we provide the data to support our recent finding on the intercalation of gold chloride underneath atomically precise graphene nanoribbons (GNRs). GNRs have a...
