Molecular Bond Engineering and Feature Learning for the Design of Hybrid Organic–Inorganic Perovskite Solar Cells with Strong Noncovalent Halogen–Cation Interactions

Hybrid organic–inorganic perovskites
are exceedingly interesting
candidates for new solar energy technologies for both ground-based
and space applications. However, their large-scale production is hampered
by the lack of long-term stability, mostly associated with ion migration.
The specific role of noncovalent bonds in contributing to the stability
remains elusive, and in certain cases controversial. Here, we perform
an investigation on a large perovskite chemical space via a combination
of first-principles calculations for the bond strengths and the recently
developed sure independent screening and sparsifying operator (SISSO)
algorithm. The latter is used to formulate mathematical descriptors
that, by highlighting the importance of specific noncovalent molecular
bonds, can guide the design of perovskites with suppressed ion migration.
The results unveil the distinct nature of different noncovalent interactions,
with remarkable differences compared to previous arguments and interpretations
in the literature on the basis of smaller chemical spaces. In particular,
we clarify the origin of the higher stability offered by formamidinium
compared to methylammonium, which shows to be different from previous
arguments in the literature, and the reasons for the improved stability
given by the halogen F and explain the exceptional case of overall
stronger bonds for guanidiunium. Within the stability boundaries given
by the Goldschmidt factor, the found descriptors give an all-in-one
picture of noncovalent interactions which provide more stable configurations,
also including interactions other than H bonds. Such descriptors are
more informative than previously used quantities and can be used as
a universal input to better inform new machine learning studies.

有机-无机杂化钙钛矿（hybrid organic–inorganic perovskites）是极具应用潜力的新型太阳能技术候选材料，可同时适配地面与航天应用场景。然而，其大规模生产受制于长期稳定性不足的缺陷，该问题主要与离子迁移相关。非共价键（noncovalent bonds）对稳定性的具体贡献机制仍尚不明确，部分案例中甚至存在争议。本文结合第一性原理计算（first-principles calculations，用于评估键合强度）与新近提出的独立筛选与稀疏算子（sure independent screening and sparsifying operator, SISSO）算法，对大规模钙钛矿化学空间开展研究。后者被用于构建数学描述符，通过凸显特定非共价分子键的重要性，可指导设计具备抑制离子迁移特性的钙钛矿材料。研究结果揭示了不同非共价相互作用的独特本质，与此前基于较小化学空间的文献中的相关论断与解读存在显著差异。具体而言，本文阐明了甲脒相较于甲胺具备更高稳定性的根源——该结论与现有文献中的相关论点截然不同——同时解释了卤素氟元素可提升材料稳定性的原因，以及胍阳离子能形成整体更强键合作用的特殊案例。在戈尔德施密特因子（Goldschmidt factor）划定的稳定性边界内，本文所得描述符可全面展现非共价相互作用的全貌，涵盖除氢键（H bonds）之外的其他相互作用类型。此类描述符比此前使用的量化指标更具信息价值，可作为通用输入参数为后续机器学习研究提供更充分的参考依据。