Publications de Michele Ceriotti

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of molecules and condense ...

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear fun ...

The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...

This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation ...

Many applications in computational science require computing the elements of a function of a large matrix. A commonly used approach is based on the the evaluation of the eigenvalue decomposition, a task that, in general, involves a computing time that scal ...

Age hardening induced by the formation of (semi)-coherent precipitate phases is crucial for the processing and final properties of the widely used Al-6000 alloys. Early stages of precipitation are particularly important from the fundamental and technologic ...

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 ...

Michele Ceriotti

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Improving sample and feature selection with principal covariates regression

Robustness of Local Predictions in Atomistic Machine Learning Models

Surface segregation in high-entropy alloys from alchemical machine learning

Assessment of Approximate Methods for Anharmonic Free Energies

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

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

Quantum fluctuations and isotope effects in ab initio descriptions of water

Simulating the ghost: quantum dynamics of the solvated electron

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

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

Probing the effects of broken symmetries in machine learning

A new kind of atlas of zeolite building blocks

Uncertainty quantification by direct propagation of shallow ensembles

Machine Learning at the Atomic Scale

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Chemiscope: interactive structure-property explorer for materials and molecules

Atom-density representations for machine learning

Evaluating functions of positive-definite matrices using colored-noise thermostats

Ab initio Modelling of the Early Stages of Precipitation in Al-6000 Alloys

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