322 citations · 323 across the 3 of their papers we have counts for
8 papers
Learning electron densities in the condensed phase
Alan M. Lewis, Andrea Grisafi, Michele Ceriotti +1
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which e…
Physics-inspired structural representations for molecules and materials
Felix Musil, Andrea Grisafi, Albert P. Bartók +3
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic scale structure of matter and…
Multi-scale approach for the prediction of atomic scale properties
Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Electronic nearsightedness is one of the fundamental principles governing the behavior of condensed matter and supporting its description in terms of local entities such as chemica…
Incorporating long-range physics in atomic-scale machine learning
Andrea Grisafi, Michele Ceriotti
The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk…
Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
Nathaniel Raimbault, Andrea Grisafi, Michele Ceriotti +1
Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecul…
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…