10 citations · 20 across the 4 of their papers we have counts for
7 papers
Unconstrained learning of equivariant atomistic properties
Joseph W. Abbott, Paolo Pegolo, Pol Febrer +6
Machine-learned interatomic potentials based on graph neural networks (GNNs) have made first-principles accuracy routine for the potential energy surface (PES). Their backbones, of…
Overcoming the limitations of body-ordered potentials for atomistic machine learning
Filippo Bigi, Michelangelo Domina, Maximilian Lloyd Ach +5
Machine-learning interatomic potentials have become indispensable tools in atomistic simulations. The most popular models rely heavily on physical priors, such as locality, smoothn…
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
How unconstrained machine-learning models learn physical symmetries
Michelangelo Domina, Joseph William Abbott, Paolo Pegolo +2
The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-le…
High-quality, high-information datasets for universal atomistic machine learning
Cesare Malosso, Filippo Bigi, Paolo Pegolo +7
The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many wide…
Metatensor and metatomic: foundational libraries for interoperable atomistic machine learning
Filippo Bigi, Joseph W. Abbott, Philip Loche +12
Incorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce the computational c…
Roadmap on Advancements of the FHI-aims Software Package
Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush +203
Electronic-structure theory is the foundation of the description of materials including multiscale modeling of their properties and functions. Obviously, without sufficient accurac…