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Michele Ceriotti

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Related publications (162)

Finite-temperature materials modeling from the quantum nuclei to the hot electron regime

Michele Ceriotti, Chiheb Ben Mahmoud, Nataliya Lopanitsyna

Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindere ...

2021

2020 JCP Emerging Investigator Special Collection

Michele Ceriotti

AIP Publishing2021

Origins of structural and electronic transitions in disordered silicon

Michele Ceriotti, Chiheb Ben Mahmoud

2021

Machine learning for metallurgy III: A neural network potential for Al-Mg-Si

Albert Johann Glensk, William Curtin, Michele Ceriotti, Daniel John Gilles Marchand, Abhinav Jain

High-strengthmetal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by atomic scale phenomena such as shearing of the precipitates by dislocations ...

2021

Efficient implementation of atom-density representations

Till Junge, Félix Benedito Clément Musil, Michele Ceriotti, Michael John Willatt, Max David Veit, Alexander Jan Goscinski, Guillaume André Jean Fraux

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materi ...

2021

Multi-scale approach for the prediction of atomic scale properties

Michele Ceriotti, Andrea Grisafi, Jigyasa Nigam

Electronic nearsightedness is one of the fundamental principles that governs the behavior of condensed matter and supports its description in terms of local entities such as chemical bonds. Locality also underlies the tremendous success of machine-learning ...

2021

The role of feature space in atomistic learning

Michele Ceriotti, Giulio Imbalzano, Alexander Jan Goscinski, Guillaume André Jean Fraux

Efficient, physically-inspired descriptors of the structure and composition of molecules and materials play a key role in the application of machine-learning techniques to atomistic simulations. The proliferation of approaches, as well as the fact that eac ...

2021

Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...

AMER CHEMICAL SOC2021

Reply to: On the liquid-liquid phase transition of dense hydrogen

Michele Ceriotti, Bingqing Cheng

NATURE PORTFOLIO2021

Local invertibility and sensitivity of atomic structure-feature mappings

Michele Ceriotti, Sergey Pozdnyakov

Background: The increasingly common applications of machine-learning schemes to atomic-scale simulations have triggered efforts to better understand the mathematical properties of the mapping between the Cartesian coordinates of the atoms and the variety o ...

2021

Importance of Nuclear Quantum Effects for NMR Crystallography

Michele Ceriotti, Venkat Kapil, Edgar Albert Engel

The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of computational predictions of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential ...

AMER CHEMICAL SOC2021

Bayesian probabilistic assignment of chemical shifts in organic solids

Michele Ceriotti, Manuel Cordova, David Lyndon Emsley, Martins Balodis, Bruno Simões de Almeida

A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional ...

2021

Chemical physics software

Michele Ceriotti

AIP Publishing2021

Modeling the Ga/As binary system across temperatures and compositions from first principles

Michele Ceriotti, Giulio Imbalzano

Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to ach ...

AMER PHYSICAL SOC2021

Simulating the ghost: quantum dynamics of the solvated electron

Michele Ceriotti, Piero Gasparotto, Jinggang Lan, Venkat Kapil

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

Quantum vibronic effects on the electronic properties of solid and molecular carbon

François Gygi, Michele Ceriotti, Han Yang

We study the effect of quantum vibronic coupling on the electronic properties of carbon allotropes, including molecules and solids, by combining path integral first principles molecular dynamics (FPMD) with a colored noise thermostat. In addition to avoidi ...

AMER PHYSICAL SOC2021

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

Learning Electron Densities in the Condensed Phase

Michele Ceriotti, Andrea Grisafi

We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centered auxiliary basis, which enables an accurate expansion of the all-electron density in a form suitable ...

AMER CHEMICAL SOC2021

Physics-Inspired Structural Representations for Molecules and Materials

Félix Benedito Clément Musil, Michele Ceriotti, Andrea Grisafi

The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atom ...

AMER CHEMICAL SOC2021

Machine learning meets chemical physics

Michele Ceriotti

Over recent years, the use of statistical learning techniques applied to chemical problems has gained substantial momentum. This is particularly apparent in the realm of physical chemistry, where the balance between empiricism and physics-based theory has ...

AMER INST PHYSICS2021

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