Publications by Kevin Rossi | EPFL Graph Search

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Predicting when phase changes occur in nanoparticles is fundamental for designing the next generation of devices suitable for catalysis, biomedicine, optics, chemical sensing and electronic circuits. The estimate of the temperature at which metallic nanopa ...

Understanding the catalyst compositional and structural features that control selectivity is of uttermost importance to target desired products in chemical reactions. In this joint experimental-computational work, we leverage tailored Cu/ZnO precatalysts a ...

We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict faradaic efficiencies for 11 CO2 reduction reaction prod ...

We investigate the impact of the formation process of Cu nanoparticles on the distribution of adsorption sites and hence on their activity. Using molecular dynamics, we model formation pathways characteristic of physical synthesis routes as the annealing o ...

A non-trivial interplay rules the relationship between the structure and the chemophysical properties of a nanoparticle. In this context, characterization experiments, molecular dynamics simulations and electronic structure calculations may allow the varia ...

Machine-learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale, and complexity. Given the interpolative nature of these models, the r ...

Carbon nanostructures formed through physical synthesis come in a variety of sizes and shapes. With the end goal of rationalizing synthetic pathways of carbon nanostructures as a function of tunable parameters in the synthesis, we investigate how the initi ...

We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this framework in the prediction of formation energies and atomic forces in molecul ...

Nanocatalyst-by-design promises to empower the next generation of electrodes for energy devices. However, current numerical methods consider individual and often geometrical closed-shell nanoparticles, neglecting how the coexistence of several and structur ...

The focus of this short review is directed towards investigations of the dynamics of nanostructured metallic heterogeneous catalysts and the evolution of interfaces during reaction-namely, the metal-gas, metal-liquid, and metal-support interfaces. Indeed, ...

Colloidal atomic layer deposition (c-ALD) enables the growth of hybrid organic/inorganic oxide shells with tunable thickness at the nanometer scale around ligand-functionalized inorganic nanoparticles (NPs). This recently developed method has demonstrated ...

Machine learning algorithms have recently emerged as a tool to generate force fields which display accuracies approaching the ones of the ab-initio calculations they are trained on, but are much faster to compute. The enhanced computational speed of machin ...

We develop a multi-scale approach towards the design of metallic nanoparticles with applications as catalysts in electrochemical reactions. The here discussed method exploits the relationship between nanoparticle architecture and electrochemical activity a ...

We present a generally applicable computational framework for the efficient and accurate characterization of molecular structural patterns and acid properties in an explicit solvent using H2O2 and CH3SO3H in phenol as an example. To address the challenges ...

Li-S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulfide (LiPS) shuttling. Computational screening of L ...

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

We show that, contrary to popular assumptions, predictions from machine learning potentials built upon highdimensional atom-density representations almost exclusively occur in regions of the representation space which lie outside the convex hull defined by ...

Establishing a unified framework for describing the structures of molecular and periodic systems is a long-standing challenge in physics, chemistry, and material science. With the rise of machine learning methods in these fields, there is a growing need fo ...

The electrochemical CO2 reduction reaction (CO2RR) holds the promise to revolutionize the chemical industry by producing value-added chemicals and fuels from CO2 and water while storing renewable energy. However, catalyst design still remains one of the ma ...

Efficient theoretical methods for the structural analysis of nanoparticles are very much needed. Here the authors demonstrate the use of machine-learning force fields and of a data-driven approach to study the thermodynamical stability and elucidate the me ...

Kevin Rossi

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A universal signature in the melting of metallic nanoparticles

Elucidating the structure-dependent selectivity of CuZn towards methane and ethanol in CO2 electroreduction using tailored Cu/ZnO precatalysts

What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective

Born to be different: the formation process of Cu nanoparticles tunes the size trend of the activity for CO2 to CH4 conversion

Structural characterisation of nanoalloys for (photo)catalytic applications with the Sapphire library

Uncertainty estimation for molecular dynamics and sampling

Modeling and characterization of the nucleation and growth of carbon nanostructures in physical synthesis

Compact atomic descriptors enable accurate predictions via linear models

Structural Screening and Design of Platinum Nanosamples for Oxygen Reduction

Realistic Modelling of Dynamics at Nanostructured Interfaces Relevant to Heterogeneous Catalysis

Colloidal-ALD Grown Hybrid Shells Nucleate via a Ligand–Precursor Complex

On machine learning force fields for metallic nanoparticles

Correlating Oxygen Reduction Reaction Activity and Structural Rearrangements in MgO-Supported Platinum Nanoparticles

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

Accelerating the theoretical study of Li-polysulfide adsorption on single-atom catalysts via machine learning approaches

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Exploring the robust extrapolation of high-dimensional machine learning potentials

Representations and descriptors unifying the study of molecular and bulk systems

Well-Defined Copper-Based Nanocatalysts for Selective Electrochemical Reduction of CO2 to Ethylene and Ethanol

Data-driven simulation and characterisation of gold nanoparticle melting