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 acceler...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.展开更多
基金Work at CMU was supported by the National Science Foundation(NSF)Division of Materials Research through grant DMR-2021803This research used resources of the Argonne Leadership Computing Facility(ALCF),which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357of the National Energy Research Scientific Computing Center(NERSC),a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy,under Contract DE-AC02-05CH11231.
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.
