14 citations · 38 across the 8 of their papers we have counts for
11 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…
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…
Journal Research Data Policies in Materials Science
Lukas Hörmann, Hemanadhan Myneni, Rwayda Kh. S. Al-Hamd +17
Open and reproducible research in materials science relies on the availability of data, code, and common metadata standards. Journal research data policies (RDPs) remain a primary…
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…
Pushing the limits of unconstrained machine-learned interatomic potentials
Filippo Bigi, Paolo Pegolo, Arslan Mazitov +2
Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The…
Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations
Manuel Grumet, Takeru Miyagawa, Olivier Pittet +4
Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fas…
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…
Representing spherical tensors with scalar-based machine-learning models
Michelangelo Domina, Filippo Bigi, Paolo Pegolo +1
Rotational symmetry plays a central role in physics, providing an elegant framework to describe how the properties of 3D objects -- from atoms to the macroscopic scale -- transform…
Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
Divya Suman, Jigyasa Nigam, Sandra Saade +5
Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, di…
PET-MAD, a lightweight universal interatomic potential for advanced materials modeling
Arslan Mazitov, Filippo Bigi, Matthias Kellner +6
Machine-learning interatomic potentials (MLIPs) have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of…