308 citations · 600 across the 4 of their papers we have counts for
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physics.comp-ph2018★ 308 cited
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…
physics.chem-ph2018★ 170 cited
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…