人工智能驱动的材料搜索引擎
Artificial Intelligence Powered Material Search Engine
摘要：
由于人工智能（AI）的最新突破，材料科学中的许多数据驱动应用已经成为可能。随着X射线衍射、各种光谱学和显微镜数据等材料数据数量的增长，人工智能在材料工程中的应用变得越来越可行。在这项工作中，我们已经报告了一个材料搜索引擎，使用的原子间空间（d值）从X射线衍射提供材料信息。我们已经研究了各种技术预测的前景材料使用X射线衍射数据。我们使用随机森林，朴素贝叶斯（高斯）和神经网络算法来实现这一点。这些算法的平均准确率分别为88.50%、100.0%和88.89%。最后，我们将所有这些技术组合成一个集成方法，使预测更通用。这种集成方法具有~ 100%的准确率。此外，我们正在设计一个基于图神经网络（GNN）的架构，以提高可解释性和准确性。因此，我们希望解决传统的基于词典和基于元数据的材料搜索引擎的计算和时间复杂度，并提供更通用的预测。
英文摘要：
Many data-driven applications in material science have been made possible because of recent breakthroughs in artificial intelligence(AI). The use of AI in material engineering is becoming more viable as the number of material data such as X-Ray diffraction, various spectroscopy, and microscope data grows. In this work, we have reported a material search engine that uses the interatomic space (d value) from X-ray diffraction to provide material information. We have investigated various techniques for predicting prospective material using X-ray diffraction data. We used the Random Forest, Naive Bayes (Gaussian), and Neural Network algorithms to achieve this. These algorithms have an average accuracy of 88.50\%, 100.0\%, and 88.89\%, respectively. Finally, we combined all these techniques into an ensemble approach to make the prediction more generic. This ensemble method has a ~100\% accuracy rate. Furthermore, we are designing a graph neural network (GNN)-based architecture to improve interpretability and accuracy. Thus, we want to solve the computational and time complexity of traditional dictionary-based and metadata-based material search engines and to provide a more generic prediction.
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。
