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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... -
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... -
Projectability disentanglement for accurate and automated electronic-structur...
Maximally-localized Wannier functions (MLWFs) are a powerful and broadly used tool to characterize the electronic structure of materials, from chemical bonding to dielectric... -
Projectability disentanglement for accurate and automated electronic-structur...
Maximally-localized Wannier functions (MLWFs) are a powerful and broadly used tool to characterize the electronic structure of materials, from chemical bonding to dielectric... -
Sampling the materials space for conventional superconducting compounds
We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive... -
In Silico Design of Porous Polymer Networks: High Throughput Screening for Me...
Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable... -
High-throughput screening of Weyl semimetals
Topological Weyl semimetals represent a novel class of non-trivial materials, where band crossings with linear dispersions take place at generic momenta across reciprocal space.... -
High-throughput screening of Weyl semimetals
Topological Weyl semimetals represent a novel class of non-trivial materials, where band crossings with linear dispersions take place at generic momenta across reciprocal space.... -
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... -
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... -
ReDD-COFFEE: A ready-to-use database of covalent organic framework structures...
Covalent organic frameworks (COFs) are a versatile class of nanoporous materials that can be used for a broad range of applications. They possess strong covalent bonds and low... -
Second-harmonic generation tensors from high-throughput density-functional pe...
Optical materials play a key role in enabling modern optoelectronic technologies in a wide variety of domains such as the medical or the energy sector. Among them, nonlinear... -
Benchmarking the GW100 dataset with the Yambo code by means of G₀W₀ approxima...
In this work we provide the results for IP and EA of all the 100 molecules of the set as computed within the Yambo code. In this way, we enlarge the GW100 benchmark considering... -
High throughput inverse design and Bayesian optimization of functionalities: ...
The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this work, we have built a database of ab initio calculated SS... -
AiiDA 1.0, a scalable computational infrastructure for automated reproducible...
The ever-growing availability of computing power and sustained development of advanced computational methods have contributed much to recent scientific progress. These... -
Towards high-throughput many-body perturbation theory: efficient algorithms a...
The automation of ab initio simulations is essential in view of performing high-throughput (HT) computational screenings oriented to the discovery of novel materials with... -
A new dataset of 415k stable and metastable materials calculated with the PBE...
In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE)... -
A new dataset of 175k stable and metastable materials calculated with the PBE...
In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE)... -
A new dataset of 175k stable and metastable materials calculated with the PBE...
In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE)... -
Symmetry-based computational search for novel binary and ternary 2D materials
We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a combinatorial engine' that constructs...
