225 citations · 521 across the 7 of their papers we have counts for
7 papers
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…
Gaussian Process States: A data-driven representation of quantum many-body physics
Aldo Glielmo, Yannic Rath, Gabor Csanyi +2
We present a novel, non-parametric form for compactly representing entangled many-body quantum states, which we call a `Gaussian Process State'. In contrast to other approaches, we…
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…
Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels
Aldo Glielmo, Peter Sollich, Alessandro De Vita
We present a novel scheme to accurately predict atomic forces as vector quantities, rather than sets of scalar components, by Gaussian Process (GP) Regression. This is based on mat…