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Publications associées (29)

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Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Mohammad Khaja Nazeeruddin

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

Elsevier Science Sa2024

Modelling of metal alloys in realistic conditions with machine learning

Nataliya Lopanitsyna

Computer simulations based on statistical methods have emerged as a powerful tool for studying structure-property relationships at the atomistic level. However, to provide reliable insights into materials in realistic conditions, it is essential to accurat ...

EPFL2023

Bacterial inactivation in sunlit surface waters is dominated by reactive species that emanate from the synergy between light, iron, and natural organic matter

César Pulgarin, Stefanos Giannakis, Jun Ma, Da Wang, Shuang Song

In this study, the synergistic and antagonistic effects of Fe species and coexisting natural organic matter (NOM) on the efficacy of solar light disinfection of water are investigated. Different initial iron species (Fe2+/Fe3+) and naturalorganic matter ty ...

Elsevier2023

A data-driven interpretation of the stability of organic molecular crystals

Michele Ceriotti, Edgar Albert Engel, Maria Pakhnova

Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fr ...

ROYAL SOC CHEMISTRY2023

Modeling non-covalent interactions in condensed phase

Veronika Juraskova

The modeling of non-covalent interactions, solvation effects, and chemical reactions in complex molecular environment is a challenging task. Current state-of-the-art approaches often rely on static computations using implicit solvent models and harmonic ap ...

EPFL2022

Hidden-nucleons neural-network quantum states for the nuclear many-body problem

Giuseppe Carleo

We generalize the hidden-fermion family of neural network quantum states to encompass both continuous and discrete degrees of freedom and solve the nuclear many-body Schrodinger equation in a systematically improvable fashion. We demonstrate that adding hi ...

AMER PHYSICAL SOC2022

Reactivity of Multimetallic Thorium Nitrides Generated by Reduction of Thorium Azides

Rosario Scopelliti, Marinella Mazzanti, Luciano Barluzzi, Fang-Che Hsueh

Thorium nitrides are likely intermediates in the reported cleavage and functionalization of dinitrogen by molecular thorium complexes and are attractive compounds for the study of multiple bond formation in f-element chemistry, but only one example of thor ...

2022

The formation of the milky way halo and its dwarf satellites: a NLTE-1D abundance analysis. IV. Segue 1, Triangulumii, and Coma Berenices UFDs

Pascale Jablonka

We present atmospheric parameters and abundances for chemical elements from carbon to barium in metal-poor stars in Segue 1 (seven stars), Coma Berenices (three stars), and Triangulum II (one star) ultrafaint dwarf galaxies (UFDs). The effective temperatur ...

OXFORD UNIV PRESS2021

Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworks

Berend Smit, Seyedmohamad Moosavi, Daniele Ongari, Kevin Maik Jablonka

Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can f ...

NATURE RESEARCH2021