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Studies on the regularity of perovskite formation via machine learning - ScienceDirect
Source: sciencedirect.com
Article summary:
1. Machine learning was used to select out the regularity of perovskite formation.
2. Factors affecting the formation of perovskite were identified and new ABX3 and A2B'B''X6 candidate perovskites materials were found.
3. The accuracy of the machine learning model is as high as 96.55% for ABX3 independent test set and 91.83% for A2B'B''X6 compounds.
Article analysis:
The article “Studies on the Regularity of Perovskite Formation via Machine Learning” provides an overview of how machine learning can be used to identify factors that affect the formation of perovskites, as well as to predict whether certain compounds will form a perovskite structure or not. The article is written in a clear and concise manner, making it easy to understand for readers with varying levels of knowledge about machine learning and perovskites. The authors provide evidence from previous studies to support their claims, which adds credibility to their findings.
However, there are some potential biases in the article that should be noted. For example, the authors focus mainly on how machine learning can be used to identify factors that affect the formation of perovskites, but they do not discuss other methods that could be used for this purpose (e.g., high-throughput DFT). Additionally, while the authors provide evidence from previous studies to support their claims, they do not explore any counterarguments or alternative perspectives on their findings. Furthermore, while the authors mention possible risks associated with using machine learning for predicting perovskite formation (e.g., overfitting), they do not provide any details about these risks or how they can be mitigated.
In conclusion, while this article provides an interesting overview of how machine learning can be used to identify factors that affect the formation of perovskites and predict whether certain compounds will form a perovskite structure or not, there are some potential biases that should be noted when evaluating its trustworthiness and reliability.