820 citations · 12k across the 115 of their papers we have counts for
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Modeling high-entropy transition-metal alloys with alchemical compression
Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer +2
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mec…
A data-driven interpretation of the stability of molecular crystals
Rose K. Cersonsky, Maria Pakhnova, Edgar A. Engel +1
Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blo…
A smooth basis for atomistic machine learning
Filippo Bigi, Kevin Huguenin-Dumittan, Michele Ceriotti +1
Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighbourhood of each atom in t…
Beyond potentials: integrated machine-learning models for materials
Michele Ceriotti
Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energ…
Electronic-structure properties from atom-centered predictions of the electron density
Andrea Grisafi, Alan M. Lewis, Mariana Rossi +1
The electron density of a molecule or material has recently received major attention as a target quantity of machine-learning models. A natural choice to construct a model that yie…
Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four body interactions"
Sergey N. Pozdnyakov, Michael J. Willatt, Albert P. Bartók +3
The "quasi-constant" SOAP and ACSF fingerprint manifolds recently discovered by Parsaeifard and Goedecker are a direct consequence of the presence of degenerate pairs of configurat…