10 citations · 35 across the 8 of their papers we have counts for
9 papers
Comparing the latent features of universal machine-learning interatomic potentials
Sofiia Chorna, Davide Tisi, Cesare Malosso +3
The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential energy surface ac…
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…
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…
Uncertainty in the era of machine learning for atomistic modeling
Federico Grasselli, Sanggyu Chong, Venkat Kapil +2
The widespread adoption of machine learning surrogate models has significantly improved the scale and complexity of systems and processes that can be explored accurately and effici…
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…
Adaptive energy reference for machine-learning models of the electronic density of states
Wei Bin How, Sanggyu Chong, Federico Grasselli +2
The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and ele…
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…