Publications by Giulio Imbalzano

The role of feature space in atomistic learning

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

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

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

Uncertainty estimation for molecular dynamics and sampling

Michele Ceriotti, Venkat Kapil, Yongbin Zhuang, Kevin Rossi, Edgar Albert Engel, Federico Grasselli, Giulio Imbalzano

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

Transferable machine-learning models of complex materials: the case of GaAs

Giulio Imbalzano

Molecular simulations allow to investigate the behaviour of materials at the atomistic level, shedding light on phenomena that cannot be directly observed in experiments. Accurate results can be obtained with ab initio methods, while simulations of large-s ...

EPFL2021

3D Ordering at the Liquid-Solid Polar Interface of Nanowires

Anna Fontcuberta i Morral, Michele Ceriotti, Cécile Hébert, Duncan Thomas Lindsay Alexander, Gözde Tütüncüoglu, Martin George Friedl, Luca Francaviglia, Mahdi Zamani, Nicolas Tappy, Giulio Imbalzano

The nature of the liquid-solid interface determines the characteristics of a variety of physical phenomena, including catalysis, electrochemistry, lubrication, and crystal growth. Most of the established models for crystal growth are based on macroscopic t ...

Wiley2020

Data-driven many-body representations with chemical accuracy for molecular simulations from the gas to the condensed phase

Michele Ceriotti, Giulio Imbalzano

AMER CHEMICAL SOC2018

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions

Michele Ceriotti, Giulio Imbalzano

The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase in computer power accompanied by advances in correlated electronic structur ...

2018

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

Michele Ceriotti, Giulio Imbalzano, Andrea Anelli, Daniele Giofré, Sinja Klees

2018