In this study, we introduced Matini-Net, which is a versatile framework for feature engineering and automated architecture design for materials informatics research using deep neural networks. Matini-Net provides the flexibility to design feature-based, graph-based, and combinations of these models, accommodating both single- and multimodal model architectures. For validation, we performed a performance evaluation on the MatBench benchmarking dataset of five properties, targeting five types of regression architectures that can be designed using Matini-Net. When applied to each of the five material property datasets, the best model performance for the various architectures exhibited R2 > 0.84. This highlights the usefulness and flexibility of Matini-Net for accelerating materials discovery. Specifically, this framework was developed for researchers with limited experience in deep learning to easily apply it to research through automated feature engineering, hyperparameter tuning, and network construction. Moreover, Matini-Net improves the model interpretability by performing an importance analysis of the selected features. We believe that by employing Matini-Net, machine and deep learning can be applied more easily and effectively in various types of materials research.

中文翻译：

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Matini-Net：用于特征工程和深度神经网络设计的多功能材料信息学研究框架。


在这项研究中，我们介绍了 Matini-Net，它是一个多功能框架，用于使用深度神经网络进行材料信息学研究的特征工程和自动化架构设计。Matini-Net 提供了设计基于特征、基于图和这些模型组合的灵活性，适用于单模态和多模态模型架构。为了验证，我们对 MatBench 基准测试数据集的五个属性进行了性能评估，针对可以使用 Matini-Net 设计的五种回归架构。当应用于 5 个材料属性数据集中的每一个时，各种架构的最佳模型性能为 R2 > 0.84。这凸显了 Matini-Net 在加速材料发现方面的实用性和灵活性。具体来说，该框架是为深度学习经验有限的研究人员开发的，他们可以通过自动化特征工程、超参数调整和网络构建轻松将其应用于研究。此外，Matini-Net 通过对所选特征进行重要性分析来提高模型的可解释性。我们相信，通过使用 Matini-Net，机器学习和深度学习可以更轻松、更有效地应用于各种类型的材料研究。