Publications related to Balancing Density Functional Theory Interaction Energies in Charged Dimers Precursors to Organic Semiconductors

Automated all-functionals infrared and Raman spectra

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

Nonempirical semilocal density functionals for correcting the self-interaction of polaronic states

Alfredo Pasquarello, Stefano Falletta

Through the use of the piecewise-linearity condition of the total energy, we correct the self-interaction for the study of polarons by constructing nonempirical functionals at the semilocal level of theory. We consider two functionals, the gamma DFT and mu ...

Aip Publishing2024

Charge-Transfer States in Organic Nanowires

Lucile Annie Chassat

Charge separation processes in organic semiconductors play a pivotal role in diverse applications ranging from photovoltaics to photocatalysis. Understanding these mechanisms, particularly the role of hybrid charge-transfer (CT) states, is essential for ad ...

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

Electronic transport in graphene with out-of-plane disorder

Oleg Yazyev, Yifei Guan

Real-world samples of graphene often exhibit various types of out-of-plane disorder-ripples, wrinkles and folds-introduced at the stage of growth and transfer processes. These complex out-of-plane defects resulting from the interplay between self-adhesion ...

Nature Portfolio2024

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

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

Controlling On-Surface Photoactivity: The Impact of π-Conjugation in Anhydride-Functionalized Molecules on a Semiconductor Surface

Karl-Heinz Ernst, Christian Wäckerlin

On-surface synthesis has become a prominent method for growing low-dimensional carbon-based nanomaterials on metal surfaces. However, the necessity of decoupling organic nanostructures from metal substrates to exploit their properties requires either trans ...

Wiley-VCH Verlag GmbH2024

Molecular hypergraph neural networks

Philippe Schwaller, Junwu Chen

Graph neural networks (GNNs) have demonstrated promising performance across various chemistry-related tasks. However, conventional graphs only model the pairwise connectivity in molecules, failing to adequately represent higher order connections, such as m ...

Aip Publishing2024

Theoretical explanation of passivation behavior in OFP-Cu and OF-Cu

Du Wen, Na Li

This research presents a comprehensive comparative analysis of the passivation kinetics of OFP-Cu and OF-Cu in simulated repository electrolyte. The study employs a range of techniques, including potentiodynamic polarization, multi-step potentiostatic pola ...

Pergamon-Elsevier Science Ltd2024