配体(生物化学)
启发式
理论(学习稳定性)
材料科学
化学
纳米技术
计算机科学
化学物理
机器学习
生物化学
受体
操作系统
作者
Zih‐Yu Lin,Jiaonan Sun,Stephen B. Shiring,Letian Dou,Brett M. Savoie
标识
DOI:10.1002/ange.202305298
摘要
Abstract Two‐dimensional (2D) halide perovskites are an attractive class of hybrid perovskites that have additional optoelectronic tunability due to their accommodation of relatively large organic ligands. Nevertheless, contemporary ligand design depends on either expensive trial‐and‐error testing of whether a ligand can be integrated within the lattice or conservative heuristics that unduly limit the scope of ligand chemistries. Here, the structural determinants of stable ligand incorporation within Ruddlesden‐Popper (RP) phase perovskites are established by molecular dynamics (MD) simulations of over ten‐thousand RP‐phase perovskites and training of machine learning classifiers capable of predicting structural stability based solely on generalizable ligand features. The simulation results show near‐perfect predictions of positive and negative literature examples, predict trade‐offs between several ligand features and stability, and ultimately predict an inexhaustibly large 2D‐compatible ligand design‐space.
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