论文标题
基于卷积神经网络的纳米材料的跨学科发现
Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks
论文作者
论文摘要
材料科学文献包含材料的最新和全面的科学知识。但是，它们的内容是非结构化和多样的，从而在提供足够的材料设计和合成的信息方面存在很大的差距。为此，我们使用基于卷积神经网络（CNN）的自然语言处理（NLP）和计算机视觉（CV）技术来发现有关纳米材料和能量材料相关的出版物中的有价值的基于实验性的信息。我们的第一个系统Textmaster从文本中提取意见，并将其分类为挑战和机遇，分别达到94％和92％的准确性。我们的第二个系统，GraphMaster，从具有98.3 \％分类精度和4.3％数据提取均方根误差的出版物中实现了表和数字的数据提取。我们的结果表明，这些系统可以通过评估综合见解和案例分析的详细参考来评估材料对某些应用的适用性。这项工作为科学文献的采矿知识提供了新的视角，从而通过CNN提供了广泛的色板，以加速纳米材料研究。
The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a significant gap in providing sufficient information for material design and synthesis. To this end, we used natural language processing (NLP) and computer vision (CV) techniques based on convolutional neural networks (CNN) to discover valuable experimental-based information about nanomaterials and synthesis methods in energy-material-related publications. Our first system, TextMaster, extracts opinions from texts and classifies them into challenges and opportunities, achieving 94% and 92% accuracy, respectively. Our second system, GraphMaster, realizes data extraction of tables and figures from publications with 98.3\% classification accuracy and 4.3% data extraction mean square error. Our results show that these systems could assess the suitability of materials for a certain application by evaluation of synthesis insights and case analysis with detailed references. This work offers a fresh perspective on mining knowledge from scientific literature, providing a wide swatch to accelerate nanomaterial research through CNN.
