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physics.comp-ph2020
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…
physics.comp-ph2018
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…