-
Efficient Kr/Xe separation from triangular g-C3N4 nanopores: density-function...
Poly(triazine imide) or PTI is a promising material for molecular sieving membranes, thanks to its atom-thick ordered lattice with an extremely high density (1.6 × 10^14... -
Pivotal role of intersite Hubbard interactions in Fe-doped α-MnO₂
We present a first-principles investigation of the structural, electronic, and magnetic properties of the pristine and Fe-doped α-MnO₂ using density-functional theory with... -
Determining interface structures in vertically aligned nanocomposite films
Vertically aligned nanocomposites (VANs) films have self-assembled pillar-matrix nanostructures. Owing to their large area-to-volume ratios, interfaces in VAN films are... -
The AiiDA-Spirit plugin for automated spin-dynamics simulations and multi-sca...
Landau-Lifshitz-Gilbert (LLG) spin-dynamics calculations based on the extended Heisenberg Hamiltonian is an important tool in computational materials science involving magnetic... -
Dataset of proximity induced superconductivity in a topological insulator
Interfacing a topological insulator (TI) with an s-wave superconductor (SC) is a promising material platform that offers the possibility to realize a topological superconductor... -
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... -
One-shot approach for enforcing piecewise linearity on hybrid functionals: ap...
We present an efficient procedure for constructing nonempirical hybrid functionals to accurately predict band gaps of extended systems. We determine mixing parameters by... -
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... -
Optimizing accuracy and efficacy in data-driven materials discovery for the s...
The production of hydrogen fuels, via water splitting, is of practical relevance for meeting global energy needs and mitigating the environmental consequences of... -
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... -
High-throughput computation of Raman spectra from first principles
Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its... -
Anti-symmetric Compton scattering in LiNiPO4: Towards a direct probe of the m...
We present a combined theoretical and experimental investigation of the anti-symmetric Compton profile in LiNiPO4 as a possible probe for magneto-electric toroidal moments.... -
Machine learning guided high-throughput search of non-oxide garnets
Garnets, known since the early stages of human civilization, have found important applications in modern technologies including magnetorestriction, spintronics, lithium... -
Graphene nanoribbons with mixed cove-cape-zigzag edge structure
A recently developed bottom-up synthesis strategy enables the fabrication of graphene nanoribbons with well-defined width and non-trivial edge structures from dedicated... -
Hubbard U through polaronic defect states
Since the preliminary work of Anisimov and co-workers, the Hubbard corrected DFT+U functional has been used for predicting properties of correlated materials by applying on-site... -
Importance of intersite Hubbard interactions in β-MnO2: A first-principles DF...
We present a first-principles investigation of the structural, electronic, and magnetic properties of pyrolusite (β-MnO2) using conventional and extended Hubbard-corrected... -
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... -
Gas transport across carbon nitride nanopores: a comparison of van der Waals ...
C2N is an ordered two-dimensional carbon nitride with a high density (1.7 × 10^14 cm−2) of 3.1 Å-sized nanopores, making it promising for high-flux gas sieving for... -
Crystal-graph attention networks for the prediction of stable materials
Graph neural networks have enjoyed great success in the prediction of material properties for both molecules and crystals. These networks typically use the atomic positions... -
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...