Publications related to ONETEP plus TOSCAM: Uniting Dynamical Mean Field Theory and Linear-Scaling Density Functional Theory

Related publications (37)

Cooperative promotion of electroreduction of CO to n-propanol by *CO enrichment and proton regulation

The CO2/CO electroreduction reaction (CO2RR/CORR) to liquid products presents an enticing pathway to store intermittent renewable electricity. However, the selectivity for desirable high-value C3 products, such as n-propanol, remains unsatisfactory in the ...

ROYAL SOC CHEMISTRY2025

Lagnet: Better Electron Density Prediction for Lcao-based Data and Drug-like Substances

Egor Rumiantsev

The electron density is an important object in quantum chemistry that is crucial for many downstream tasks in drug design. Recent deep learning approaches predict the electron density around a molecule from atom types and atom positions. Most of these meth ...

BMC2025

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

Florian Mathieu Pascal Viscosi, Ruben Rodriguez Madrid, David Lyndon Emsley, Michele Ceriotti, Matthias Linus Kellner, Jacob Brian Holmes, Yuxuan Zhang

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

AMER CHEMICAL SOC2025

Orbital-Resolved DFT plus U for Molecules and Solids

Nicola Marzari, Iurii Timrov

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

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

Ksenia Briling

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

Automated all-functionals infrared and Raman spectra

Nicola Marzari, Lorenzo Bastonero

Infrared and Raman spectroscopies are ubiquitous techniques employed in many experimental laboratories, thanks to their fast and non-destructive nature able to capture materials' features as spectroscopic fingerprints. Nevertheless, these measurements freq ...

Nature Portfolio2024

Experimental and ab initio derivation of interface stress in nanomultilayered coatings: Application to immiscible Cu/W system with variable in-plane stress

Claudia Cancellieri, Pandula Manura Liyanage

Interface stress is a fundamental descriptor for interphase boundaries and is defined in strict relation to the interface energy. In nanomultilayers with their intrinsically high interface density, the functional properties are dictated by the interface st ...

Elsevier2024

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

Magnetism of Single Surface Adsorbed Atoms Studied with Radio-Frequency STM

Clément Marie Soulard

This thesis investigates the magnetic properties of single atoms and molecules adsorbed on thin magnesium oxide decoupling layers, grown on a silver single crystal. To address these systems experimentally, we use a low temperature scanning tunneling micros ...

EPFL2024

Magnetic orderings and electronic structure of complex oxides from advanced Hubbard functionals

Luca Binci

Transition metal oxides represent a class of materials displaying very unusual electronic, structural and magnetic properties. They are extremely interesting, both from a technological and fundamental point of view. The most important characteristic of the ...

EPFL2023

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Justin Villard

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 ...

EPFL2023

Tuning the reactivity of uranium towards small molecules in multimetallic complexes

Nadir Jori

One of the main goals of organometallic chemistry in the last decades was the activation of small molecule in mild reaction conditions. Even though multiple examples of catalytic cycles able to produce fine chemicals from cheap and abundant sources using t ...

EPFL2023

Hybridization driving distortions and multiferroicity in rare-earth nickelates

Nicola Marzari, Iurii Timrov, Michele Kotiuga, Luca Binci

For decades transition-metal oxides have generated a huge interest due to the multitude of physical phenomena they exhibit. In this class of materials, the rare-earth nickelates, RNiO3, stand out for their rich phase diagram stemming from complex couplings ...

Amer Physical Soc2023

Murunskite - a new class of functional material

Davor Tolj

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 - ...

EPFL2023

Spectral and thermodynamic properties of interacting electrons with dynamical functionals

Tommaso Chiarotti

Empowered by ever-increasing computational power and algorithmic developments, electronic-structure simulations continue to drive research and innovation in materials science. In this context, ab-initio calculations offer an unbiased platform for the under ...

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

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

Unraveling the Effect of the Chemical and Structural Composition of ZnxNi1-xFe2O4 on the Electron Transfer at the Electrochemical Interface

Mallikarjun Madagalam, Sandro Carrara

In order to deepen the understanding of the role of transition metal oxides in electron transfer at the electrochemical interface, the performance of ZnxNi1-xFe2O4 (x = 0, 0.2, 0.4, 0.6, 0.8, 1) nanomaterials in electrochemical sensing is studied. Nanomate ...

WILEY2023

A low-temperature prismatic slip instability in Mg understood using machine learning potentials

William Curtin, Xin Liu, Binglun Yin

Prismatic slip in magnesium at temperatures T

PERGAMON-ELSEVIER SCIENCE LTD2023

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