The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials.Online material databases have been instrumental in exploring one...The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials.Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials,namely thermodynamic stability.However,the vibrational stability,which is another aspect of synthesizability,of new materials is not known.Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable.Here,a dataset of vibrational stability for~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials.This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.展开更多
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 supported by computational resources provided by the Australian Government through the National Computa-tional Infrastructure(NCI)National Facility and the Pawsey Supercomputer Centre,under the NCMAS scheme.This research used resources of the National Energy Research Scientific Computing Center(NERSC)a U.S.Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory,operated under Contract No.DE-AC02-05CH11231.
文摘The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials.Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials,namely thermodynamic stability.However,the vibrational stability,which is another aspect of synthesizability,of new materials is not known.Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable.Here,a dataset of vibrational stability for~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials.This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.
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.
