Deep materials informatics: Applications of deep learning in materials science
Deep materials informatics: Applications of deep learning in materials science
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DOI：
10.1557/mrc.2019.73
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发表时间：
2019-09-01
影响因子：
1.9
通讯作者：
Choudhary, Alok
中科院分区：
文献类型：
--
作者：
Agrawal, Ankit;Choudhary, Alok
数据驱动分析在材料科学中的应用越来越多，导致了材料信息学的兴起。在数据分析的竞技场中，深度学习在过去几年中已经成为一种改变游戏规则的技术，实现了许多现实世界的应用，例如自动驾驶汽车。在本文中，作者概述了深度学习，其优势，挑战以及最近在不同类型材料数据上的应用。材料数据库和大数据的可用性越来越高，沿着深度学习的突破性进展，为加速下一代材料的发现、设计和部署提供了很大的希望。
The growing application of data-driven analytics in materials science has led to the rise of materials informatics. Within the arena of data analytics, deep learning has emerged as a game-changing technique in the last few years, enabling numerous real-world applications, such as self-driving cars. In this paper, the authors present an overview of deep learning, its advantages, challenges, and recent applications on different types of materials data. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in deep learning offers a lot of promise to accelerate the discovery, design, and deployment of next-generation materials.