Publications related to A generalized-gradient approximation exchange hole model for dispersion coefficients

Related publications (31)

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Mathieu Salzmann, Alexandre Massoud Alahi, Megh Hiren Shukla

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

2024

Range-separated hybrid functionals for accurate prediction of band gaps of extended systems

Alfredo Pasquarello, Stefano Falletta, Jing Yang

In this work, we systematically evaluate the accuracy in band gap prediction of range-separated hybrid functionals on a large set of semiconducting and insulating materials and carry out comparisons with the performance of their global counterparts. We obs ...

NATURE PORTFOLIO2023

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

Tobias Kober, Davide Piccini

Purpose: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-enco ...

Wiley2023

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

Chiheb Ben Mahmoud

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 ...

EPFL2023

The convexity condition of density-functional theory

Edward Baxter Linscott

It has long been postulated that within density-functional theory (DFT), the total energy of a finite electronic system is convex with respect to electron count so that 2E(v)[N-0]

Aip Publishing2023

Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature

Alfredo Pasquarello, Patrick Gono, Thomas Bischoff, Haiyuan Wang

We develop a computationally efficient scheme to accurately determine finite-temperature band gaps for metal halide perovskites belonging to the class ABX(3) (A = Rb, Cs; B = Ge, Sn, Pb; and X = F, Cl, Br, I). First, an initial estimate of the band gap is ...

NATURE PORTFOLIO2022

A Machine Learning-based Framework for Forecasting Sales of New Products With Short Life Cycles Using Deep Neural Networks

Ralf Seifert, Yara Kayyali El Alem

Demand forecasting is becoming increasingly important as firms launch new products with short life cycles more frequently. This paper provides a framework based on state-of-the-art techniques that enables firms to use quantitative methods to forecast sales ...

2022

Roadmap on Machine learning in electronic structure

Michele Ceriotti, Andrea Grisafi, Max David Veit, Jigyasa Nigam

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by ...

IOP Publishing Ltd2022

Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning

Michael Lehning, Jérôme François Sylvain Dujardin

Predicting wind flow in highly complex terrain like the Alps is a challenge for all models. When physical processes need to be resolved in a spatially explicit manner, grids with high horizontal resolution of a few hundred meters are often required and dra ...

WILEY2022

Virtual metrology applied to milling process

Paul Arthur Adrien Pierre Dreyfus

Production quality and process efficiency are the two main drivers that lead any industrial strategy. To ensure product quality, a duality historically existed between two approaches, namely batch sampling and systematic sampling. In batch sampling, the ba ...

EPFL2022

Worldwide performance evaluation of ground surface reflectance models

Christophe Ballif, Arttu Matias Tuomiranta

To improve the accuracy of bifacial gain estimation, recent radiative models of solar energy systems have abandoned the traditional assumption of isotropic ground-reflected radiance. However, surface reflectance itself is still commonly considered as a con ...

2021

Discrete-Choice Mining of Social Processes

Victor Kristof

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...

EPFL2021

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Fernando Porté Agel, Jiannong Fang

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real ...

2021

Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure Waveform via Deep Learning

Nikolaos Stergiopoulos, Vasiliki Bikia, Georgios Rovas, Stamatia Zoi Pagoulatou

Determination of left ventricular (LV) end-systolic elastance (E- es ) is of utmost importance for assessing the cardiac systolic function and hemodynamical state in humans. Yet, the clinical use of E- es is not established due to the invasive nature and h ...

FRONTIERS MEDIA SA2021

Development of indoor environmental quality index using a low-cost monitoring platform

Dusan Licina

The fundamental goal of modern high-performance buildings is to achieve low environmental carbon footprint while securing indoor conditions that promote human health and well-being. Current procedures for indoor environmental quality (IEQ) assessment are o ...

ELSEVIER SCI LTD2021

Extensive Benchmarking of DFT+U Calculations for Predicting Band Gaps

Iurii Timrov

Accurate computational predictions of band gaps are of practical importance to the modeling and development of semiconductor technologies, such as (opto)electronic devices and photoelectrochemical cells. Among available electronic-structure methods, densit ...

2021

Thermal spin crossover in Fe(ii) and Fe(iii). Accurate spin state energetics at the solid state

Sergio Vela Llausi, Maria Fumanal Quintana

The thermal spin crossover (SCO) phenomenon refers to an entropy-driven spin transition in some materials based on d(6)-d(9) transition metal complexes. While its molecular origin is well known, intricate SCO behaviours are increasingly common, in which th ...

ROYAL SOC CHEMISTRY2020

An automated calibration framework and open source tools for 3D lake hydrodynamic models

Alfred Johny Wüest, Damien Bouffard, Andrea Cimatoribus, Theo Baracchini

Understanding lake dynamics is crucial to provide scientifically credible information for ecosystem management. In this context, three-dimensional hydrodynamic models are a key information source to assess critical but often subtle changes in lake dynamics ...

2020

Learning the electronic density of states in condensed matter

Michele Ceriotti, Andrea Anelli, Chiheb Ben Mahmoud

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 ...

AMER PHYSICAL SOC2020

Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Sabine Süsstrunk, Gökhan Yildirim

Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural im ...

2020