1 citations · 1 across the 2 of their papers we have counts for
4 papers
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,…
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…
Universal Machine Learning Interatomic Potentials are Ready for Phonons
Antoine Loew, Dewen Sun, Hai-Chen Wang +2
There has been an ongoing race for the past several years to develop the best universal machinelearning interatomic potential. This progress has led to increasingly accurate models…