There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and str...There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and stresses,combining innovative architectures with big data.Here,we benchmark these models on their ability to predict harmonic phonon properties,which are critical for understanding the vibrational and thermal behavior of materials.Using around 10000 ab initio phonon calculations,we evaluate model performance across various phonon-related parameters to test the universal applicability of these models.The results reveal that some models achieve high accuracy in predicting harmonic phonon properties.However,others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium.These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.展开更多
基金funding from the Horizon Europe MSCA Doctoral network grant n.101073486, EUSpecLab, funded by the European UnionS.B. and D.S. acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the project BO4280/11-1. H.C.W and M.A.L.M would like to thank the NHR Center PC2 for providing computing time on the Noctua 2 supercomputers.
文摘There has been an ongoing race for the past several years to develop the best universal machine learning interatomic potential.This progress has led to increasingly accurate models for predicting energy,forces,and stresses,combining innovative architectures with big data.Here,we benchmark these models on their ability to predict harmonic phonon properties,which are critical for understanding the vibrational and thermal behavior of materials.Using around 10000 ab initio phonon calculations,we evaluate model performance across various phonon-related parameters to test the universal applicability of these models.The results reveal that some models achieve high accuracy in predicting harmonic phonon properties.However,others still exhibit substantial inaccuracies,even if they excel in the prediction of the energy and the forces for materials close to dynamical equilibrium.These findings highlight the importance of considering phonon-related properties in the development of universal machine learning interatomic potentials.
