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Minimizing Density Functional Failures for Non-covalent Interactions beyond van der Waals Complexes

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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 ...

EPFL2024

Efficient and insightful descriptors for representing molecular and material space

Alexander Jan Goscinski

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 ...

EPFL2024

Orbital-Resolved DFT plus U for Molecules and Solids

Nicola Marzari, Iurii Timrov, Eric Macke

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 ...

Amer Chemical Soc2024

Roughness Evolution Induced by Third-Body Wear

Jean-François Molinari, Antonio Joaquin Garcia Suarez, Tobias Brink

Surface roughness is a key factor when it comes to friction and wear, as well as to other physical properties. These phenomena are controlled by mechanisms acting at small scales, in which the topography of apparently flat surfaces is revealed. Roughness i ...

New York2024

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

Mario Geiger

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 ...

IOP Publishing Ltd2023

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Berend Smit, Senja Dominique Barthel, Amber Kashan Mace

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 ...

Washington2023

Correlation properties of a one-dimensional repulsive Bose gas at finite temperature

Riccardo Rota

We present a comprehensive study shedding light on how thermal fluctuations affect correlations in a Bose gas with contact repulsive interactions in one spatial dimension. The pair correlation function, the static structure factor, and the one-body density ...

IOP Publishing Ltd2023

Factorized structure of the long-range two-electron integrals tensor and its application in quantum chemistry

Laura Grigori

We introduce two new approximation methods for the numerical evaluation of the long-range component of the range-separated Coulomb potential and the approximation of the resulting high dimensional Two-Electron Integrals tensor (TEI) with long-range interac ...

San Diego2023

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

Giovanni Pizzi, Antimo Marrazzo, Junfeng Qiao, Miki Bonacci, Andrea Ferretti

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 ...

NATURE PORTFOLIO2023

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A density wave (DW) is a fundamental type of long-range order in quantum matter tied to self-organization into a crystalline structure. The interplay of DW order with superfluidity can lead to complex scenarios that pose a great challenge to theoretical an ...

NATURE PORTFOLIO2023