Publications de Kevin Rossi | EPFL Graph Search

Alloying as a Strategy to Boost the Stability of Copper Nanocatalysts during the Electrochemical CO2 Reduction Reaction

Raffaella Buonsanti, Anna Loiudice, Kevin Rossi, Valery Okatenko, Dragos-Constantin Stoian, Anastasia Blokhina, Alexander Nicolas Chen, Mark Adrian Newton

2023

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

Kevin Rossi

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 ...

ELSEVIER2023

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

Kevin Rossi

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 ...

ROYAL SOC CHEMISTRY2023

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

Kevin Rossi

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 ...

AMER PHYSICAL SOC2023

Divide-and-conquer potentials enable scalable and accurate predictions of forces and energies in atomistic systems

Andrea Anelli, Kevin Rossi

In committee of experts strategies, small datasets are extracted from a larger one and utilised for the training of multiple models. These models' predictions are then carefully weighted so as to obtain estimates which are dominated by the model(s) that ar ...

Royal Soc Chemistry2023

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

Kevin Rossi

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 ...

WILEY2022

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

Kevin Rossi

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 ...

ROYAL SOC CHEMISTRY2022

Realistic Modelling of Dynamics at Nanostructured Interfaces Relevant to Heterogeneous Catalysis

Kevin Rossi

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, ...

MDPI2022

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

David Lyndon Emsley, Raffaella Buonsanti, Anna Loiudice, Snaedis Björgvinsdóttir, Kevin Rossi, Michael Allan Hope, Ona Segura Lecina, Amrit Venkatesh

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 ...

2022

Exploring the robust extrapolation of high-dimensional machine learning potentials

Kevin Rossi

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 ...

AMER PHYSICAL SOC2022

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

Raffaella Buonsanti, Ludovic Emile Frédy Zaza, Kevin Rossi

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 ...

2022

Data-driven simulation and characterisation of gold nanoparticle melting

Kevin Rossi

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 ...

Nature Portfolio2021

A universal signature in the melting of metallic nanoparticles

Kevin Rossi

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 ...

ROYAL SOC CHEMISTRY2021

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

Kevin Rossi, Elena Gazzarrini

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 ...

ROYAL SOC CHEMISTRY2021

Compact atomic descriptors enable accurate predictions via linear models

Kevin Rossi

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 ...

AIP Publishing2021

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

Raffaella Buonsanti, Seyedehbehnaz Varandili, Yannick Thomas Guntern, Jan Vávra, James Richard Pankhurst, Kevin Rossi, Dragos-Constantin Stoian

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 ...

2021

Uncertainty estimation for molecular dynamics and sampling

Michele Ceriotti, Venkat Kapil, Giulio Imbalzano, Edgar Albert Engel, Kevin Rossi, Yongbin Zhuang, Federico Grasselli

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 ...

2021

Structural Screening and Design of Platinum Nanosamples for Oxygen Reduction

Kevin Rossi

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 ...

AMER CHEMICAL SOC2020

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

Anne-Clémence Corminboeuf, Michele Ceriotti, Veronika Juraskova, Kevin Rossi

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 ...

AMER CHEMICAL SOC2020

On machine learning force fields for metallic nanoparticles

Kevin Rossi

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 ...

2019