Publication

Transferable machine-learning models of complex materials: the case of GaAs

Publications associées (31)

Efficient and insightful descriptors for representing molecular and material space

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

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

Structure and dynamics of liquid water from ab initio simulations: adding Minnesota density functionals to Jacob's ladder

Ursula Röthlisberger, Justin Villard, Martin Peter Bircher

The accurate representation of the structural and dynamical properties of water is essential for simulating the unique behavior of this ubiquitous solvent. Here we assess the current status of describing liquid water using ab initio molecular dynamics, wit ...

Royal Soc Chemistry2024

Thermal transport of glasses via machine learning driven simulations

Federico Grasselli

Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...

Frontiers Media Sa2024

Solvation Free Energies from Machine Learning Molecular Dynamics

Nicola Marzari, Nicéphore Arthur François Bonnet

The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...

Amer Chemical Soc2024

A diversity-driven discovery of potential nanoporous materials for energy-related applications

Sauradeep Majumdar

The design and discovery of potential novel materials is critical for the advancement of climate change mitigation technologies. In this respect, metal-organic frameworks (MOFs) have received considerable attention over the last two decades. The combinatio ...

EPFL2024

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Amer Chemical Soc2024

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Sandip De, Nataliya Lopanitsyna, Guillaume André Jean Fraux, Arslan Mazitov

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Iop Publishing Ltd2024

Machine learning-aided generative molecular design

Philippe Schwaller, Jeff Guo

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Michele Ceriotti, Federico Grasselli

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

Amer Physical Soc2024

High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

Roya Moghaddasi Fereidani

Molecular quantum dynamics simulations are essential for understanding many fundamental phenomena in physics and chemistry. They often require solving the time-dependent Schrödinger equation for molecular nuclei, which is challenging even for medium-sized ...

EPFL2024

A Versatile Approach to Stabilize Liquid-Liquid Interfaces using Surfactant Self-Assembly

Julian Charles Shillcock

Stabilizing liquid-liquid interfaces, whether between miscible or immiscible liquids, is crucial for a wide range of applications, including energy storage, microreactors, and biomimetic structures. In this study, a versatile approach for stabilizing the w ...

Wiley-V C H Verlag Gmbh2024

Band Gap Renormalization at Different Symmetry Points in Perovskites

Majed Chergui, Lijie Wang

Using ultrafast broad-band transient absorption (TA) spectroscopy of photoexcited MAPbBr3 thin films with probe continua in the visible and the mid- to deep-ultraviolet (UV) ranges, we capture the ultrafast renormalization at the fundamental gap at the R s ...

Amer Chemical Soc2024

Second harmonic scattering of nanoparticles: A journey into the electrical double layer

Bingxin Chu

In aqueous solutions, a charged surface causes the redistribution of nearby ions. The ion layers formed are known as the electrical double layer (EDL), and are widespread in many systems involving electrochemistry, colloidal science, biomedicine, and energ ...

EPFL2024

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Alexandre Massoud Alahi, Mariam Ahmed Mahmoud Hegazy Hassan, Mohamed Ossama Ahmed Abdelfattah

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

2024

'Fraternal-twin' ferroelectricity: competing polar states in hydrogen-doped samarium nickelate from first principles

Michele Kotiuga

In ferroelectric switching, an applied electric field switches the system between two polar symmetry-equivalent states. In this work, we use first-principles calculations to explore the polar states of hydrogen-doped samarium nickelate (SNO) at a concentra ...

Iop Publishing Ltd2024

Unique corrosion behavior of an archaeological Roman iron ring: Microchemical characterization and thermodynamic considerations

Stefano Mischler, Laura Brambilla

The corrosion mechanisms of a Roman iron bezel ring were investigated by in-depth characterization of its uncommon corrosion pattern and thermodynamic modelling. A silver foil and altered glass remnants were identified, covered with thick strata of magneti ...

2024

Organic matters, but inorganic matters too: column examination of elevated mercury sorption on low organic matter aquifer material using concentrations and stable isotope ratios

Lorenz Schwab

Sorption of mercury (Hg) in soils is suggested to be predominantly associated with organic matter (OM). However, there is a growing collection of research that suggests that clay minerals and oxides are also important solid phases for the sorption of solu ...

2024

Computational Studies on the Stability of Organic-Inorganic Hybrid Halide Lead Perovskites

Mathias Dankl

Perovskite-based solar cells are currently the most rapidly advancing photovoltaic technology but concerns about their long-term stability are still impeding full-scale commercialization. This thesis provides computational insights into some of the stabili ...

EPFL2024

Exciton migration in two-dimensional materials

Excitons play an essential role in the optical response of two-dimensional materials. These are bound states showing up in the band gaps of many-body systems and are conceived as quasiparticles formed by an electron and a hole. By performing real-time simu ...

Nature Portfolio2024