Accelerated searches,made possible by machine learning techniques,are of growing interest in materials discovery.A suitable case involves the solution processing of components that ultimately form thin films of solar ...Accelerated searches,made possible by machine learning techniques,are of growing interest in materials discovery.A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites(HOIPs).The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large“compositional”space(at times,exceeding 500,000 possible combinations).Selecting a HOIP with desirable characteristics involves choosing different cations,halides,and solvent blends from a diverse palette of options.An unguided search by experimental investigations or molecular simulations is prohibitively expensive.In this work,we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce,and in which the search space is given by binary variables indicating whether a constituent is present or not.We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time(less than 10%)needed to complete an exhaustive search.We find an optimal composition within 15±10 iterations in a HOIP compositional space containing 72 combinations,and within 31±9 iterations when considering mixed halides(240 combinations).Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach.This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.展开更多
基金This work was partially supported by the Cornell Center for Materials Research with funding from the NSF MRSEC program(DMR-1719875)through a seed funding award.H.H.and M.P.were partially supported by this award.P.C.,P.F.,W.H.,and M.P.were partially supported by NSF(CMMI-1536895,DMR-1719875,DMR-1120296,CMMI-1254298)by AFOSR(FA9550-15-1-0038).
文摘Accelerated searches,made possible by machine learning techniques,are of growing interest in materials discovery.A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic–inorganic perovskites(HOIPs).The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large“compositional”space(at times,exceeding 500,000 possible combinations).Selecting a HOIP with desirable characteristics involves choosing different cations,halides,and solvent blends from a diverse palette of options.An unguided search by experimental investigations or molecular simulations is prohibitively expensive.In this work,we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce,and in which the search space is given by binary variables indicating whether a constituent is present or not.We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time(less than 10%)needed to complete an exhaustive search.We find an optimal composition within 15±10 iterations in a HOIP compositional space containing 72 combinations,and within 31±9 iterations when considering mixed halides(240 combinations).Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach.This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.
