| 1 | Machine learning the Hubbard U parameter in DFT+U using Bayesian optimization显示文摘Within density functional theory(DFT),adding a Hubbard U correction can mitigate some of the deficiencies of local and semi-local exchange-correlation functionals,while maintaining computational efficiency.However,the accuracy of DFT+U largely depends on the chosen Hubbard U values.We propose an approach to determining the optimal U parameters for a given material by machine learning.The Bayesian optimization(BO)algorithm is used with an objective function formulated to reproduce the band structures produced by more accurate hybrid functionals.This approach is demonstrated for transition metal oxides,europium chalcogenides,and narrow-gap semiconductors.The band structures obtained using the BO U values are in agreement with hybrid functional results.Additionally,comparison to the linear response(LR)approach to determining U demonstrates that the BO method is superior. | Maituo Yu Shuyang Yang Chunzhi Wu Noa Marom | 2020 | npj Computational Materials2020，，1： | 1 |
| 2 | Finding predictive models for singlet fission by machine learning显示文摘Singlet fission(SF),the conversion of one singlet exciton into two triplet excitons,could significantly enhance solar cell efficiency.Molecular crystals that undergo SF are scarce.Computational exploration may accelerate the discovery of SF materials.However,many-body perturbation theory(MBPT)calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening.We use the sure-independence-screening-and-sparsifying-operator(SISSO)machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons(PAH101).SISSO generates models by iteratively combining physical primary features.The best models are selected by linear regression with cross-validation.The SISSO models successfully predict the SF driving force with errors below 0.2 eV.Based on the cost,accuracy,and classification performance of SISSO models,we propose a hierarchical materials screening workflow.Three potential SF candidates are found in the PAH101 set. | Xingyu Liu Xiaopeng Wang Siyu Gao Vincent Chang Rithwik Tom Maituo Yu Luca M.Ghiringhelli Noa Marom | 2022 | npj Computational Materials2022，，1： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员