144 citations · 310 across the 5 of their papers we have counts for
7 papers · 1 filter
Efficient implementation of atom-density representations
Félix Musil, Max Veit, Alexander Goscinski +5
Physically-motivated and mathematically robust atom-centred representations of molecular structures are key to the success of modern atomistic machine learning (ML) methods. They l…
On the Completeness of Atomic Structure Representations
Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók +3
Many-body descriptors are widely used to represent atomic environments in the construction of machine learned interatomic potentials and more broadly for fitting, classification an…
Path-integral dynamics of water using curvilinear centroids
George Trenins, Michael J. Willatt, Stuart C. Althorpe
We develop a path-integral dynamics method for water that resembles centroid molecular dynamics (CMD), except that the centroids are averages of curvilinear, rather than cartesian,…
Atomic-scale representation and statistical learning of tensorial properties
Andrea Grisafi, David M. Wilkins, Michael J. Willatt +1
This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial…
Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements
Michael J. Willatt, Félix Musil, Michele Ceriotti
Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input str…
Relation of centroid molecular dynamics and ring-polymer molecular dynamics to exact quantum dynamics
Timothy J. H. Hele, Michael J. Willatt, Andrea Muolo +1
We recently obtained a quantum-Boltzmann-conserving classical dynamics by making a single change to the derivation of the `Classical Wigner' approximation. Here, we show that the f…