66 citations · 109 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
A deep learning model for chemical shieldings in molecular organic solids including anisotropy
Matthias Kellner, Jacob B. Holmes, Ruben Rodriguez-Madrid +4
Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous mo…
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…