10 citations · 14 across the 4 of their papers we have counts for
5 papers
Hierarchical universal open structure dataset for atomistic modeling
Sofiia Chorna, Cesare Malosso, Jonathan Schmidt +1
Atomic-scale simulations have been transformed by machine-learning models that predict the properties of atomistic structures with the accuracy of quantum calculations, but at a sm…
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…
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…
Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs
Sofiia Chorna, Kateryna Tarelkina, Eloïse Berthier +1
While concept-based interpretability methods have traditionally focused on local explanations of neural network predictions, we propose a novel framework and interactive tool that…
Massive Atomic Diversity: a compact universal dataset for atomistic machine learning
Arslan Mazitov, Sofiia Chorna, Guillaume Fraux +4
The development of machine-learning models for atomic-scale simulations has benefited tremendously from the large databases of materials and molecular properties computed in the pa…