标题： 用于逆向设计具有目标能级的二维杂化钙钛矿的有机分子指纹识别 显示英文标题

标题： Fingerprinting Organic Molecules for the Inverse Design of Two-Dimensional Hybrid Perovskites with Target Energetics

摘要： 人工智能（AI）辅助的工作流程已经改变了材料发现，使功能材料的化学空间得以快速探索。 具有卓越的光电特性，二维（2D）杂化钙钛矿代表了一个令人兴奋的前沿领域，但目前设计2D钙钛矿的努力主要依赖于试错法和专家直觉方法，导致大部分化学空间未被探索，并影响了具有所需特性的混合材料的设计。 在此，我们介绍了一种基于可逆指纹表示的Dion-Jacobson钙钛矿的逆向设计工作流程，该表示适用于数百万种共轭二铵有机间隔物。 通过结合高通量密度泛函理论（DFT）计算、可解释的机器学习和合成可行性筛选，我们确定了新的有机间隔物候选物，在二维混合钙钛矿中实现了有机和无机基元之间的确定性能级对齐。 这些结果突显了将可逆的、具有物理意义的分子表示整合到AI辅助设计中的强大作用，简化了混合材料的性能目标设计。 显示英文摘要

摘要： Artificial intelligence (AI)-assisted workflows have transformed materials discovery, enabling rapid exploration of chemical spaces of functional materials. Endowed with extraordinary optoelectronic properties, two-dimensional (2D) hybrid perovskites represent an exciting frontier, but current efforts to design 2D perovskites rely heavily on trial-and-error and expert intuition approaches, leaving most of the chemical space unexplored and compromising the design of hybrid materials with desired properties. Here, we introduce an inverse design workflow for Dion-Jacobson perovskites that is built on an invertible fingerprint representation for millions of conjugated diammonium organic spacers. By incorporating high-throughput density functional theory (DFT) calculations, interpretable machine learning, and synthesis feasibility screening, we identified new organic spacer candidates with deterministic energy level alignment between the organic and the inorganic motifs in the 2D hybrid perovskites. These results highlight the power of integrating invertible, physically meaningful molecular representations into AI-assisted design, streamlining the property-targeted design of hybrid materials.