Organic photovoltaic(OPV)materials are promising candidates for cheap,printable solar cells.However,there are a very large number of potential donors and acceptors,making selection of the best materials difficult.Here...Organic photovoltaic(OPV)materials are promising candidates for cheap,printable solar cells.However,there are a very large number of potential donors and acceptors,making selection of the best materials difficult.Here,we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately.We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency(PCE),open circuit potential(Voc),short circuit density(Jsc),highest occupied molecular orbital(HOMO)energy,lowest unoccupied molecular orbital(LUMO)energy,and the HOMO–LUMO gap.The most robust and predictive models could predict PCE(computed by DFT)with a standard error of±0.5 for percentage PCE for both the training and test set.This model is useful for pre-screening potential donor and acceptor materials for OPV applications,accelerating design of these devices for green energy applications.展开更多
基金the National Natural Science Foundation of China(51802241 and 91963209)the Fundamental Research Funds for the Central Universities(WUT:2019IVB055 and 2019IVA066)+1 种基金ARC Discovery Grant DP150104483,ARC Centre of Excellence in Exciton Science(CE170100026)the Australian Government through the Australian Renewable Energy Agency(ARENA).
基金the financial support from the Australian Renewable Energy Agency (ARENA)the Australian Centre for Advanced Photovoltaics (ACAP)the ARC Centre of Excellence in Exciton Science
基金This work was supported by the Australian Government through the Australian Research Council(ARC)under the Centre of Excellence scheme(project number CE170100026)This work was also supported by computational resources provided by the Australian Government through the National Computational Infrastructure National Facility and the Pawsey Supercomputer Centre.
文摘Organic photovoltaic(OPV)materials are promising candidates for cheap,printable solar cells.However,there are a very large number of potential donors and acceptors,making selection of the best materials difficult.Here,we show that machine-learning approaches can leverage computationally expensive DFT calculations to estimate important OPV materials properties quickly and accurately.We generate quantitative relationships between simple and interpretable chemical signature and one-hot descriptors and OPV power conversion efficiency(PCE),open circuit potential(Voc),short circuit density(Jsc),highest occupied molecular orbital(HOMO)energy,lowest unoccupied molecular orbital(LUMO)energy,and the HOMO–LUMO gap.The most robust and predictive models could predict PCE(computed by DFT)with a standard error of±0.5 for percentage PCE for both the training and test set.This model is useful for pre-screening potential donor and acceptor materials for OPV applications,accelerating design of these devices for green energy applications.
