10 citations · 57 across the 15 of their papers we have counts for
19 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…
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…
Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
Sanggyu Chong, Tong Jiang, Michelangelo Domina +4
In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is dir…
A universal machine learning model for the electronic density of states
Wei Bin How, Pol Febrer, Sanggyu Chong +5
In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbit…
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…
Learning the action for long-time-step simulations of molecular dynamics
Filippo Bigi, Johannes Spies, Michele Ceriotti
The equations of classical mechanics can be used to model the time evolution of countless physical systems, from the astrophysical to the atomic scale. Accurate numerical integrati…
FlashMD: long-stride, universal prediction of molecular dynamics
Filippo Bigi, Sanggyu Chong, Agustinus Kristiadi +1
Molecular dynamics (MD) provides insights into atomic-scale processes by integrating over time the equations that describe the motion of atoms under the action of interatomic force…
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…
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…
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Filippo Bigi, Marcel Langer, Michele Ceriotti
The use of machine learning to estimate the energy of a group of atoms, and the forces that drive them to more stable configurations, has revolutionized the fields of computational…
Prediction rigidities for data-driven chemistry
Sanggyu Chong, Filippo Bigi, Federico Grasselli +3
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with…
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
Filippo Bigi, Sanggyu Chong, Michele Ceriotti +1
Regression methods are fundamental for scientific and technological applications. However, fitted models can be highly unreliable outside of their training domain, and hence the qu…
Wigner kernels: body-ordered equivariant machine learning without a basis
Filippo Bigi, Sergey N. Pozdnyakov, Michele Ceriotti
Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale descri…
Fast evaluation of spherical harmonics with sphericart
Filippo Bigi, Guillaume Fraux, Nicholas J. Browning +1
Spherical harmonics provide a smooth, orthogonal, and symmetry-adapted basis to expand functions on a sphere, and they are used routinely in physical and theoretical chemistry as w…
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…