47 citations · 54 across the 10 of their papers we have counts for
5 papers · 1 filter
Universal Machine Learning Potentials under Pressure
Antoine Loew, Jonathan Schmidt, Silvana Botti +1
Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable per…
Universal Machine Learning Potential for Systems with Reduced Dimensionality
Giulio Benedini, Antoine Loew, Matti Hellstrom +2
We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically,…
MACE4IRmol: An uncertainty-aware foundation model for molecular infrared spectroscopy
Nitik Bhatia, Ondrej Krejci, Silvana Botti +2
Machine-learned interatomic potentials (MLIPs) have shown significant promise in predicting infrared spectra with high fidelity. However, the absence of general-purpose MLIPs that…
Accelerating point defect photo-emission calculations with machine learning interatomic potentials
Kartikeya Sharma, Antoine Loew, Haiyuan Wang +4
We introduce a computational framework leveraging universal machine learning interatomic potentials (MLIPs) to dramatically accelerate the calculation of photoluminescence (PL) spe…
Enhanced superconductivity in X4H15compounds via hole-doping at ambient pressure
Kun Gao, Wenwen Cui, Tiago F. T. Cerqueira +3
This study presents a computational investigation of X4H15 compounds (where X represents a metal) as potential superconductors at ambient conditions or under pressure. Through syst…