4 papers
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…