Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design

Abstract

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 R 2 > 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.