150 citations · 491 across the 9 of their papers we have counts for
9 papers
DADApy: Distance-based Analysis of DAta-manifolds in Python
Aldo Glielmo, Iuri Macocco, Diego Doimo +6
DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability…
Exploring the robust extrapolation of high-dimensional machine learning potentials
Claudio Zeni, Andrea Anelli, Aldo Glielmo +1
We show that, contrary to popular assumptions, predictions from machine learning potentials built upon high-dimensional atom-density representations almost exclusively occur in reg…
Data-driven simulation and characterisation of gold nanoparticle melting
Claudio Zeni, Kevin Rossi, Theodore Pavloudis +4
The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields,…
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…
Ranking the information content of distance measures
Aldo Glielmo, Claudio Zeni, Bingqing Cheng +2
Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between dat…
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…