1 citations · 1 across the 4 of their papers we have counts for
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Generative AI for Crystal Structures: A Review
Pierre-Paul De Breuck, Hai-Chen Wang, Gian-Marco Rignanese +2
As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases…
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,…
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…