Reproducibility in density functional theory calculations of solids

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Distributed systems, which enable the efficient processing of large-scale data and complex tasks across multiple interconnected processes, offer significant advantages over single-machine systems. These include enhanced scalability, improved fault toleran ...

We study how light scalar fields can change the stellar landscape by triggering a new phase of nuclear matter. Scalars coupled to nucleons can develop a non-trivial expectation value at finite baryon density. This sourcing of a scalar reduces the nucleon m ...

Ordinary metals contain electron liquids within well-defined 'Fermi' surfaces at which the electrons behave as if they were non-interacting. In the absence of transitions to entirely new phases such as insulators or superconductors, interactions between el ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

This directory contains open-source data and code obtained at the UNFoLD laboratory at EPFL. This code implements a new tracking method for fast deformation of flapping flags using an event-based camera. Sampled data from experimental results as well as si ...

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

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 energetic, electronic and structural properties is strongly improved, particula ...

5G New Radio (NR) has stringent demands on both performance and complexity for the design of low-density parity-check (LDPC) decoding algorithms and corresponding VLSI implementations. Furthermore, decoders must fully support the wide range of all 5G NR bl ...

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on tr ...

Control over synthetic DNA-based nanodevices can be achieved with a variety of physical and chemical stimuli. Actuation with light, however, is as advantageous as difficult to implement without modifying DNA strands with photo-switchable groups. Herein, we ...

We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion ...

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 desired physical properties. In this work, we propose algorithms and implementations ...

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation ...

The very concept of high-T-c superconductivity has originated from the discovery of superconductivity in copper oxides by Bednorz and Muller in 1986. Soon after their discovery, cuprates were recognized as undoubtedly complex and radically unconventional s ...

The subject of the present work is discovery and in-depth characterization of a new class of functional materials. Tuning of the bond polarity and orbital occupation with a goal of establishing balance between localization and delocalization of electrons - ...

Carbon nanostructures formed through physical synthesis come in a variety of sizes and shapes. With the end goal of rationalizing synthetic pathways of carbon nanostructures as a function of tunable parameters in the synthesis, we investigate how the initi ...

Hot exciton materials have the potential to improve the quantum efficiency of organic light-emitting diodes (OLEDs) by promoting high reversed intersystem crossing (hRISC) between a high-lying triplet (T-n, n >= 2) and a radiative singlet (S-m). In recent ...

This paper aims to analyze the transnational discourse behind the programmatic shift in spatial production and layout of workers’ housing in Turkey from the state-financed model of the interwar period to the aided self-help model by the introduction of the ...

Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students’ interaction data ...

Reproducibility in density functional theory calculations of solids

Chattez avec Graph Search

Optimal Byzantine Agreement with Little Cryptography

Heavy neutron stars from light scalars

Anomalous electrons in a metallic kagome ferromagnet

Efficient and insightful descriptors for representing molecular and material space

Data from: Event-based reconstruction of time-resolved centreline deformation of flapping flags

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Orbital-Resolved DFT plus U for Molecules and Solids

A Generalized Adjusted Min-Sum Decoder for 5G LDPC Codes: Algorithm and Implementation

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

A recipe for cracking the quantum scaling limit with machine learned electron densities

Controlling DNA nanodevices with light-switchable buffers

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows

Machine-learning the electronic density of states: electronic properties without quantum mechanics

Electron-phonon driven unconventional superconductivity: The role of small Fermi energies and of nonadiabatic processes

Murunskite - a new class of functional material

Modeling and characterization of the nucleation and growth of carbon nanostructures in physical synthesis

Donor-Acceptor-Donor "Hot Exciton" Triads for High Reverse Intersystem Crossing in OLEDs

From State-led to Aided Self-help: Marshall Plan and Workers’ Housing Cooperatives in Turkey

Generalisable Methods for Early Prediction in Interactive Simulations for Education