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Michele Ceriotti

Cette personne a quitté l’EPFL

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Publications associées (160)

Page 4 sur 8

Understanding How Ligand Functionalization Influences CO2 and N-2 Adsorption in a Sodalite Metal-Organic Framework

Michele Ceriotti, Wendy Lee Queen, Pascal Alexander Schouwink, Mehrdad Asgari, Olga Trukhina, Ilia Kochetygov, Rocio Semino Barbaresi

In this work, a detailed study is conducted to understand how ligand substitution influences the CO, and N-2 adsorption properties of two highly crystalline sodalite metal-organic frameworks (MOFs) known as Cu-BTT (BTT-3 = 1,3,5-benzenetristetrazolate) and ...

AMER CHEMICAL SOC2020

Neural network potential for Al-Mg-Si alloys

William Curtin, Michele Ceriotti, Till Junge, Daniele Giofré

The 6000 series Al alloys, which include a few percent of Mg and Si, are important in automotive and aviation industries because of their low weight, as compared to steels, and the fact their strength can be greatly improved through engineered precipitatio ...

Amer Physical Soc2017

Direct path integral estimators for isotope fractionation ratios

Michele Ceriotti, Bingqing Cheng

Fractionation of isotopes among distinct molecules or phases is a quantum effect which is often exploited to obtain insights on reaction mechanisms, biochemical, geochemical, and atmospheric phenomena. Accurate evaluation of isotope ratios in atomistic sim ...

American Institute of Physics2014

Improving sample and feature selection with principal covariates regression

Michele Ceriotti, Edgar Albert Engel, Benjamin Aaron Helfrecht

Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it improves the computational performance and often the transferability of a model. Here we ...

2021

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Federico Grasselli, Chiheb Ben Mahmoud, Sanggyu Chong

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Amer Chemical Soc2023

Surface segregation in high-entropy alloys from alchemical machine learning

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

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

Assessment of Approximate Methods for Anharmonic Free Energies

Michele Ceriotti, Venkat Kapil, Mariana Rossi Carvalho, Edgar Albert Engel

Quantitative evaluation of the thermodynamic properties of materials—most notably their stability, as measured by the free energy—must take into account the role of thermal and zero-point energy fluctuations. While these effects can easily be estimated wit ...

2019

Wigner kernels: Body-ordered equivariant machine learning without a basis

Michele Ceriotti, Sergey Pozdnyakov, Filippo Bigi

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that h ...

AIP Publishing2024

An In-Situ Neutron Diffraction and DFT Study of Hydrogen Adsorption in a Sodalite-Type Metal-Organic Framework, Cu-BTTri

Michele Ceriotti, Wendy Lee Queen, Pascal Alexander Schouwink, Mehrdad Asgari, Shuliang Yang, Olga Trukhina, Ilia Kochetygov, Safak Bulut, Rocio Semino Barbaresi

Herein we present a detailed study of the hydrogen adsorption properties of Cu‐BTTri, a robust crystalline metal–organic framework containing open metal‐coordination sites. Diffraction techniques, carried out on the activated framework, reveal a structure ...

2019

Simulating the ghost: quantum dynamics of the solvated electron

Michele Ceriotti, Venkat Kapil, Piero Gasparotto, Jinggang Lan

The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical ...

Nature Research2021

Machine learning for the structure–energy–property landscapes of molecular crystals

Michele Ceriotti, Sandip De, Félix Benedito Clément Musil

2018

Quantum fluctuations and isotope effects in ab initio descriptions of water

Michele Ceriotti, Lu Wang

Isotope substitution is extensively used to investigate the microscopic behavior of hydrogen bonded systems such as liquid water. The changes in structure and stability of these systems upon isotope substitution arise entirely from the quantum mechanical n ...

Amer Inst Physics2014

Probing the Unfolded Configurations of a β-Hairpin Using Sketch-Map

Michele Ceriotti

This work examines the conformational ensemble involved in beta-hairpin folding by means of advanced molecular dynamics simulations and dimensionality reduction. A fully atomistic description of the protein and the surrounding solvent molecules is used, an ...

2015

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Michele Ceriotti, Giulio Imbalzano, Andrea Anelli, Daniele Giofré

2018

Communication: Computing the Tolman length for solid-liquid interfaces

Michele Ceriotti, Bingqing Cheng

2018

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

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

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

Fast-forward Langevin dynamics with momentum flips

Michele Ceriotti, David Mark Wilkins, Mahdi Hijazi

2018

Probing the effects of broken symmetries in machine learning

Michele Ceriotti, Sergey Pozdnyakov, Marcel Florin Langer

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properti ...

2024

A new kind of atlas of zeolite building blocks

Michele Ceriotti, Benjamin Aaron Helfrecht, Rocio Semino Barbaresi

We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether ...

2019

Uncertainty quantification by direct propagation of shallow ensembles

Michele Ceriotti, Matthias Linus Kellner

Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further source of error on top of the intrinsic limitations of the experimental or the ...

2024