From the 1 of 20 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
20 papers
Machine-Learned NMR Shieldings in Molecular Solids with Built-In Hybrid-Functional Molecular Corrections
Matthias Kellner, Ruben Rodriguez-Madrid, Jacob B. Holmes +4
Fast and accurate chemical shielding estimators are essential for shielding-driven Nuclear Magnetic Resonance (NMR) crystallography. Machine-learning models for shielding predictio…
Using large language models to probe the limits of atom-centered structural descriptors
Michelangelo Domina, Michele Ceriotti
The paper uses large language models to find atomic structures that cannot be distinguished by atom‑centered symmetry‑invariant descriptors even when clusters of up to seven neighb…
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…
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…
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…
How to Train a Shallow Ensemble
Moritz Schäfer, Matthias Kellner, Johannes Kästner +1
Shallow ensembles provide a convenient strategy for uncertainty quantification in machine learning interatomic potentials, that is computationally efficient because the different e…