820 citations · 12k across the 115 of their papers we have counts for
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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…
Multi-scale approach for the prediction of atomic scale properties
Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Electronic nearsightedness is one of the fundamental principles governing the behavior of condensed matter and supporting its description in terms of local entities such as chemica…
Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Paraskevi Gkeka, Gabriel Stoltz, Amir Barati Farimani +14
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as…
Iterative unbiasing of quasi-equilibrium sampling
Federico Giberti, Bingqing Cheng, Gareth Aneurin Tribello +1
Atomistic modelling of phase transitions, chemical reactions, or other rare events that involve overcoming high free energy barriers usually entails prohibitively long simulation t…
Machine-learning of atomic-scale properties based on physical principles
Michele Ceriotti, Michael J. Willatt, Gábor Csányi
We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the li…
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…