Structured and porous materials particularly suitable as polarizing agents in dissolution dynamic nuclear polarization

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Investigating cognitive ability using action-based models of structural brain networks

Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...

OXFORD UNIV PRESS2022

Applicability of the landscape evolution model in the absence of rills

David Andrew Barry, Andrea Rinaldo, Paolo Perona, Seifeddine Jomaa, Mohsen Cheraghi, Andrea Cimatoribus

Despite numerous applications of physically-based models for incised landscapes, their applicability for overland flow on unchanneled surfaces is not known. This work challenges a widely used landscape evolution model for the case of non-uniform rainfall a ...

2022

Pivotal Role of Intersite Hubbard Interactions in Fe-Doped alpha-MnO2

Iurii Timrov

We present a first-principles investigation of the structural, electronic, and magnetic properties of the pristine and Fe-doped alpha-MnO2 using density-functional theory with extended Hubbard functionals. The onsite U and intersite V Hubbard parameters ar ...

AMER CHEMICAL SOC2022

Mapping Catalyst‐Solvent Interplay in Competing Carboamination/Cyclopropanation Reactions

Nicolai Cramer, Matthew Wodrich, Anne-Clémence Corminboeuf, Miyeon Chang, Simone Gallarati

Group 9 metals, in particular Rh III complexes with cyclopentadienyl ligands, are competent C–H activation catalysts. Recently, a Cp*Rh III catalyzed reaction of alkenes with N -enoxyphthalimides showed divergent outcome based on the solvent, with carboami ...

2022

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

Galvanic replacement reactions of Ga and In with Cu for the synthesis of bimetallic nanoparticles

Laia Castilla Amorós

The need for efficient and selective catalysts, capable of driving important conversions to build a more sustainable society, encourages the development of synthetic approaches towards new nanomaterials. Cu-based bimetallic nanoparticles (NPs) promise to f ...

EPFL2022

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

True scale-free networks hidden by finite size effects

Andrea Rinaldo, Samir Suweis, Amos Maritan

We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statisti ...

National Academy of Sciences2021

The physics of higher-order interactions in complex systems

Enrico Amico, Benedetta Franceschiello

Network representations of complex systems are limited to pairwise interactions, but real-world systems often involve higher-order interactions. This Perspective looks at the new physics emerging from attempts to characterize these interactions. ...

NATURE PORTFOLIO2021

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Martin Jaggi, Tao Lin, Sebastian Urban Stich, Sai Praneeth Reddy Karimireddy

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

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