Integrating multiple materials science projects in a single neural network
Integrating multiple materials science projects in a single neural network
复制标题
将多个材料科学项目集成到一个神经网络中
DOI：
10.1038/s43246-020-00052-8
复制
发表时间：
2020-07-30
影响因子：
7.8
通讯作者：
Oyaizu, Kenichi
中科院分区：
文献类型：
--
作者：
Hatakeyama-Sato, Kan;Oyaizu, Kenichi
在数据密集型科学中，机器学习在处理大数据方面发挥着关键作用。然而，由于难以将复杂的现实世界信息作为数字语言来处理，机器学习在材料科学领域的潜力一直受到限制。在这里，我们建议使用具有通用格式的图形形状的数据库来数字地描述几乎所有的材料科学实验数据，包括化学结构、过程、性质和自然语言。这些图形可以表达真实世界的数据，而信息损失很小。在我们的方法中，单个神经网络处理从十多个项目收集的多功能材料科学数据，而传统方法需要准备单独的模型来处理每个单独的数据库和属性。各种因素的多任务学习通过获取该领域的广泛知识，协同提高了参数的预测精度。这种积分有利于建立通用的预测模型和解决材料科学中的反问题。传统上，材料科学的机器学习是基于特定于数据库的模型，并且在可预测参数的数量上受到限制。在这里，一个多功能的基于图形的神经网络可以集成多个数据源，允许同时预测40多个参数。
In data-intensive science, machine learning plays a critical role in processing big data. However, the potential of machine learning has been limited in the field of materials science because of the difficulty in treating complex real-world information as a digital language. Here, we propose to use graph-shaped databases with a common format to describe almost any materials science experimental data digitally, including chemical structures, processes, properties, and natural languages. The graphs can express real world's data with little information loss. In our approach, a single neural network treats the versatile materials science data collected from over ten projects, whereas traditional approaches require individual models to be prepared to process each individual database and property. The multitask learning of miscellaneous factors increases the prediction accuracy of parameters synergistically by acquiring broad knowledge in the field. The integration is beneficial for developing general prediction models and for solving inverse problems in materials science. Traditionally, machine learning for materials science is based on database-specific models and is limited in the number of predictable parameters. Here, a versatile graph-based neural network can integrate multiple data sources, allowing the prediction of more than 40 parameters simultaneously.