通过结合计算、文献数据与PU学习弥合钙钛矿的可合成性差距
Bridging the Synthesizability Gap in Perovskites by Combining Computations, Literature Data, and PU Learning
AI总结：
本文结合高通量DFT计算与文献实验标签，采用PU学习策略训练分类器模型，以概率方式预测钙钛矿化合物的可合成性，并通过FAIR nanoHUB工具开放数据和模型。
摘要：
在新兴能源材料中，卤化物和硫属化物钙钛矿在过去十年中因其组成元素丰富、制造成本低以及高度可调的组成-结构-性能空间而受到广泛关注。利用密度泛函理论（DFT）计算可以在广阔的钙钛矿组成空间中导航，但这些计算不容易扩展到预测新材料的可合成性及其性能。因此，从这些计算中识别出的具有理想光电性能的组成中，只有有限数量已在实验中实现。弥合这一差距的一种方法是从实验文献中学习钙钛矿组成-结构空间与其实验室合成可能性之间的关系。在此，我们展示了将高通量DFT数据与从文献中收集的实验标签相结合，训练采用各种材料描述符的分类器模型，以预测任意给定钙钛矿化合物的可合成性的工作。我们的框架利用正样本和无标签样本（PU）学习策略，并基于DFT计算能量和先前存在的类似已合成化合物，对合成可能性进行概率估计。我们的数据和模型可以通过一个可查找、可访问、可互操作和可复现（FAIR）的nanoHUB工具方便地获取。
英文摘要：
Among emerging energy materials, halide and chalcogenide perovskites have garnered significant attention over the last decade owing to the abundance of their constituent species, low manufacturing costs, and their highly tunable composition-structure-property space. Navigating the vast perovskite compositional landscape is possible using density functional theory (DFT) computations, but they are not easily extended to predictions of the synthesizability of new materials and their properties. As a result, only a limited number of compositions identified to have desirable optoelectronic properties from these calculations have been realized experimentally. One way to bridge this gap is by learning from the experimental literature about how the perovskite composition-structure space relates to their likelihood of laboratory synthesis. Here, we present our efforts in combining high-throughput DFT data with experimental labels collected from the literature to train classifier models employing various materials descriptors to forecast the synthesizability of any given perovskite compound. Our framework utilizes the positive and unlabeled (PU) learning strategy and makes probabilistic estimates of the synthesis likelihood based on DFT- computed energies and the prior existence of similar synthesized compounds. Our data and models can be readily accessed via a Findable, Accessible, Interoperable, and Reproducible (FAIR) nanoHUB tool.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
