308 citations · 600 across the 4 of their papers we have counts for
5 papers
Modeling the Ga/As binary system across temperaturesand compositions from first principles
Giulio imbalzano, Michele Ceriotti
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 phas…
Uncertainty estimation for molecular dynamics and sampling
Giulio Imbalzano, Yongbin Zhuang, Venkat Kapil +4
Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time…
The role of feature space in atomistic learning
Alexander Goscinski, Guillaume Fraux, Giulio Imbalzano +1
Eficient, 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…
Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
Giulio Imbalzano, Andrea Anelli, Daniele Giofr é +3
Machine learning of atomic-scale properties is revolutionizing molecular modelling, making it possible to evaluate inter-atomic potentials with first-principles accuracy, at a frac…
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions
Thuong T. Nguyen, Eszter Székely, Giulio Imbalzano +5
The accurate representation of multidimensional potential energy surfaces is a necessary requirement for realistic computer simulations of molecular systems. The continued increase…