The low-cost industrial application of perovskite solar cells requires an environmentally friendly and scalable fabrication process.However,achieving high-quality perovskite layers under these requirements is challeng...The low-cost industrial application of perovskite solar cells requires an environmentally friendly and scalable fabrication process.However,achieving high-quality perovskite layers under these requirements is challenging because the multi-step optimization with multiple intercorrelated experimental variables typically requires the development of a new deposition process.To address this,we propose a two-step machine learning approach for creating a new method for perovskite deposition in ambient air and antisolvent-free processing with a low-toxicity solvent triethyl phosphate(TEP).The two-step machine learning approach integrates a precursor solubility prediction model and a device-efficiency prediction model within a Bayesian optimization framework.This framework enables the information of solubility to be passed as a constraint function when optimizing the efficiency of perovskite solar cells,facilitating a quick optimization of a TEP-based,vacuum-quenching-assisted deposition in ambient air.Furthermore,the optimal precursor solution is subsequently applied to FAPbI_(3) perovskite devices,achieving a device power conversion efficiency of 24.26%under ambient conditions(23℃and~50%relative humidity).This work demonstrates the promising potential of machine learning to expedite new fabrication processes to fulfill industrial needs.展开更多
基金funding support from the Ministry of Science and Technology of P.R.China under Award No.2024YFE0213600the National Natural Science Foundation of China under Award No.52433013,52103286+1 种基金the Shaanxi Bureau of Science and Technology under Award No.2022KWZ-07the"Shccig-Qinling"Program under the contract No.SMYJY202300321C。
文摘The low-cost industrial application of perovskite solar cells requires an environmentally friendly and scalable fabrication process.However,achieving high-quality perovskite layers under these requirements is challenging because the multi-step optimization with multiple intercorrelated experimental variables typically requires the development of a new deposition process.To address this,we propose a two-step machine learning approach for creating a new method for perovskite deposition in ambient air and antisolvent-free processing with a low-toxicity solvent triethyl phosphate(TEP).The two-step machine learning approach integrates a precursor solubility prediction model and a device-efficiency prediction model within a Bayesian optimization framework.This framework enables the information of solubility to be passed as a constraint function when optimizing the efficiency of perovskite solar cells,facilitating a quick optimization of a TEP-based,vacuum-quenching-assisted deposition in ambient air.Furthermore,the optimal precursor solution is subsequently applied to FAPbI_(3) perovskite devices,achieving a device power conversion efficiency of 24.26%under ambient conditions(23℃and~50%relative humidity).This work demonstrates the promising potential of machine learning to expedite new fabrication processes to fulfill industrial needs.
