论文标题
部分可观测时空混沌系统的无模型预测
Predicting the formation and stability of oxide perovskites by extracting underlying mechanisms using machine learning
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
The optimization of properties of perovskite oxides has drawn interest on account of their diverse areas of application. In this work, the hierarchical clustering technique is used to reduce the multi-collinearity among selected features from literature that are reported to have an effect on perovskite formation and stability. Operating on the vast composition space of double oxide perovskite compositions available in literature and online repositories, in this manuscript, an attempt has been made to extract the relationship between the composition and structure to predict their formability and stability. Machine learning (ML) classifiers are trained on these datasets to predict novel stable perovskite compositions. The study uses a vast feature space to narrow down the most important factors affecting the formability and stability in perovskite compounds. It also identifies stable compositions that have band gaps suitable for photovoltaic and photocatalytic applications. The developed random forest (RF)-based models may be extended to include the implications beyond photosensitive applications by focusing on the physico-chemical mechanisms driving the phenomena behind each application.
