摘要
Aiming toward a sustainable energy era,the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied.One promising avenue for the band engineering of active photocatalysts is the use of solid-solution alloying.However,the enormous possible configurations of multicomponent alloys hinders the experimental screening of this multidimensional material space,providing an opportunity for machine learning(ML)approaches to help accelerate the discovery of new multicomponent alloy materials.A conventional prerequisite for ML approaches is a large database of accurate material properties,which may require exhaustive computational and/or experimental resources.This study demonstrates that the screening of solidsolution alloys(up to hexanary systems)can be performed using a small database to minimize(and optimize)the number of high-level computational calculations.Specifically,we use ZnTe-based alloys as a prototypical example and employ a secure independent screening and sparsifing operator with the recently developed agreement method(α-method).Furthermore,we discuss and propose design routes to determine the optimal solid-solution ZnTe-based alloys for photoassisted water-splitting reactions.
基金
funding from the European Union’s Horizon 2020 Research and Innovation Programme under Marie Sklodowska-Curie grant agreement No.101034297
W.Jang acknowledges the support of the National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIT)(2022R1C1C200856712)
Computational resources were provided by the KISTI Supercomputing Center(KSC-2022-CRE-0206).
