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The material science literature contains up-to-date and comprehensivescientific knowledge of materials. However, their content is unstructured anddiverse, resulting in a significant gap in providing sufficient information formaterial design and synthesis. To this end, we used natural language processing(NLP) and computer vision (CV) techniques based on convolutional neuralnetworks (CNN) to discover valuable experimental-based information aboutnanomaterials and synthesis methods in energy-material-related publications.Our first system, TextMaster, extracts opinions from texts and classifies theminto challenges and opportunities, achieving 94% and 92% accuracy,respectively. Our second system, GraphMaster, realizes data extraction oftables and figures from publications with 98.3\% classification accuracy and4.3% data extraction mean square error. Our results show that these systemscould assess the suitability of materials for a certain application byevaluation of synthesis insights and case analysis with detailed references.This work offers a fresh perspective on mining knowledge from scientificliterature, providing a wide swatch to accelerate nanomaterial research throughCNN.

Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks