Publications by Michele Ceriotti

Exploring the Design Space of Machine Learning Models for Quantum Chemistry with a Fully Differentiable Framework

American Chemical Society (ACS)2025

A Deep Learning Model for Chemical Shieldings in Molecular Organic Solids Including Anisotropy

David Lyndon Emsley, Michele Ceriotti, Yuxuan Zhang

Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critical ...

AMER CHEMICAL SOC2025

Adaptive energy reference for machine-learning models of the electronic density of states

Michele Ceriotti, Sanggyu Chong, Kevin Kazuki Huguenin-Dumittan, Federico Grasselli

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

2025

PLUMED Tutorials: A collaborative, community-driven learning ecosystem

Michele Ceriotti, Guillaume André Jean Fraux

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several cha ...

AIP Publishing2025

Fast and flexible long-range models for atomistic machine learning

Michele Ceriotti, Philip Robin Loche, Kevin Kazuki Huguenin-Dumittan

Most atomistic machine learning (ML) models rely on a locality ansatz and decompose the energy into a sum of short-ranged, atom-centered contributions. This leads to clear limitations when trying to describe problems that are dominated by long-range physic ...

2025

i-PI 3.0: A flexible and efficient framework for advanced atomistic simulations

Michele Ceriotti, Guillaume André Jean Fraux

Atomic-scale simulations have progressed tremendously over the past decade, largely thanks to the availability of machine-learning interatomic potentials. These potentials combine the accuracy of electronic structure calculations with the ability to reach ...

2024

Surface segregation in high-entropy alloys from alchemical machine learning

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

Completeness of Atomic Structure Representations

Michele Ceriotti, Sergey Pozdnyakov, Jigyasa Nigam, Kevin Kazuki Huguenin-Dumittan

In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with ...

AIP Publishing2024

Prediction rigidities for data-driven chemistry

Michele Ceriotti, Philip Robin Loche, Sanggyu Chong, Filippo Bigi, Federico Grasselli

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

2024

Mechanism of Charge Transport in Lithium Thiophosphate

Michele Ceriotti, Federico Grasselli

Lithium ortho-thiophosphate (Li3PS4) has emerged as a promising candidate for solid-state electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of ...

2024