The prevalence of perovskite materials in myriad technologies is traceable to their diverse compositions, structures, and forms, variations of which bestow them with chameleon-like properties, functionality, and utility. By modifying the ABO₃ archetype perovskites through isomorphic substitution, aliovalent doping, and non-stoichiometry, as well as tailoring their form through nanostructuring, heterostructuring, superstructuring, and polymorphism, the portfolio of application opportunities for perovskite materials can be greatly expanded. The focus of this perspective is to explore the thought process by which human intelligence and experiential learning enables the discovery of a champion photocatalyst for CO₂ hydrogenation by juggling the elements in perovskite oxides and at which point this well-established approach needs a helping hand from artificial intelligence and machine learning.

钙钛矿材料在多种技术中的普遍存在可追溯到其多样化的组成，结构和形式，其变化赋予它们类似变色龙的特性，功能和实用性。通过通过同构取代，异价掺杂和非化学计量来修饰ABO ₃原型钙钛矿，并通过纳米结构，异质结构，超结构和多态性调整其形式，可以大大扩展钙钛矿材料的应用机会。该观点的重点是探索思维过程，通过该思维过程，人类的智力和体验式学习可以发现CO _2的最佳光催化剂。 通过杂化钙钛矿氧化物中的元素进行氢化反应，此时，这种成熟的方法需要人工智能和机器学习的帮助。