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

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

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, Kevin Kazuki Huguenin-Dumittan, Philip Robin Loche

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

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, Jigyasa Nigam, Sergey Pozdnyakov, 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, Federico Grasselli, Sanggyu Chong, Philip Robin Loche, Filippo Bigi

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

Electronic Excited States from Physically Constrained Machine Learning

Michele Ceriotti, Jigyasa Nigam

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

2024

Probing the effects of broken symmetries in machine learning

Michele Ceriotti, Sergey Pozdnyakov

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

Uncertainty quantification by direct propagation of shallow ensembles

Michele Ceriotti

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

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

Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

Michele Ceriotti, Alexander Jan Goscinski

The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of density functional theory (DFT). However, running large simulation cells with DFT is computationally demanding, wh ...

2024

A prediction rigidity formalism for low-cost uncertainties in trained neural networks

Michele Ceriotti, Federico Grasselli, Sanggyu Chong, Filippo Bigi

Quantifying the uncertainty of regression models is essential to ensure their reliability, particularly since their application often extends beyond their training domain. Based on the solution of a constrained optimization problem, this work proposes ‘pre ...

2024

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

Michele Ceriotti, Federico Grasselli

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 ...

Amer Physical Soc2024

Natural aging and vacancy trapping in Al-6xxx

William Curtin, Michele Ceriotti

Undesirable natural aging (NA) in Al-6xxx delays subsequent artificial aging (AA) but the size, composition, and evolution of clustering are challenging to measure. Here, atomistic details of early-stage clustering in Al-1% Mg-0.6%Si during NA are studied ...

Springer Heidelberg2023

Accelerated chemical science with AI

Philippe Schwaller, Michele Ceriotti

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it ...

Royal Soc Chemistry2023

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

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

One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects such as electrost ...

Amer Chemical Soc2023