Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with a...Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with ab initio accuracy.By replacing the costly density functional theory(DFT)computation of phonon modes with much faster MLIP phonon mode calculations,our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss.We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states.The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface.Across all the systems,we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes.This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity,opening pathways to high-throughput screening of defect-engineered materials.Ourwork not only demonstrates accelerated calculation of PL spectra with DFT accuracy,but also makes such calculations tractable for more complex materials.展开更多
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, EUSpecLabfunded by the European Union, and from the Novo Nordisk Foundation Data Science Research Infrastructure 2022 Grant: A high-performance computing infrastructure for data-driven research on sustainable energy materials, Grant no. NNF22OC0078009+1 种基金F.N. has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 899987K.S.T. is a Villum Investigator supported by VILLUM FONDEN (grant no. 37789).
文摘Weintroduce acomputational framework leveraging universal machine learning interatomic potentials(MLIPs)to dramatically accelerate the calculation of photoluminescence(PL)spectra of atomic or molecular emitters with ab initio accuracy.By replacing the costly density functional theory(DFT)computation of phonon modes with much faster MLIP phonon mode calculations,our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss.We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states.The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface.Across all the systems,we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes.This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity,opening pathways to high-throughput screening of defect-engineered materials.Ourwork not only demonstrates accelerated calculation of PL spectra with DFT accuracy,but also makes such calculations tractable for more complex materials.
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.
