225 citations · 521 across the 7 of their papers we have counts for
5 papers · 1 filter
Compact atomic descriptors enable accurate predictions via linear models
Claudio Zeni, Kevin Rossi, Aldo Glielmo +1
We probe the accuracy of linear ridge regression employing a three-body local density representation derived from the atomic cluster expansion. We benchmark the accuracy of this fr…
On Machine Learning Force Fields for Metallic Nanoparticles
Claudio Zeni, Kevin Rossi, Aldo Glielmo +1
Machine learning algorithms have recently emerged as a tool to generate force fields which display accuracies approaching the ones of the ab-initio calculations they are trained on…
Building nonparametric -body force fields using Gaussian process regression
Aldo Glielmo, Claudio Zeni, Ádám Fekete +1
Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackle…
Building machine learning force fields for nanoclusters
Claudio Zeni, Kevin Rossi, Aldo Glielmo +4
We assess Gaussian process (GP) regression as a technique to model interatomic forces in metal nanoclusters by analysing the performance of 2-body, 3-body and many-body kernel func…
Efficient nonparametric -body force fields from machine learning
Aldo Glielmo, Claudio Zeni, Alessandro De Vita
We provide a definition and explicit expressions for -body Gaussian Process (GP) kernels which can learn any interatomic interaction occurring in a physical system, up to -bo…