Publications de Giulio Imbalzano

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Giulio Imbalzano

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

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

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

Uncertainty estimation for molecular dynamics and sampling

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

The role of feature space in atomistic learning

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

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