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Npj Comput. Mater.: 与材料科学的碰撞:深度学习的近况

npj 知社学术圈 2022-11-14
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被视为材料科学与工程的四大关键要素“加工-结构-特征-性能(Processing-structure-property-performance)”很大程度上受物质结构或现象的空间或时间尺度影响。例如,结构信息的范围可以涵盖详细的原子坐标、物质相在微尺度的空间分布(微观结构)、碎片连通性(中尺度)、以及各种图像和图谱。这导致材料科学研究高度复杂,在各部分之间建立联系是一项具有挑战性的任务。近年来,由于实验设备自动化的快速发展以及超级计算机的进步,可公开获取的材料数据的规模呈指数级增长。这种爆发式增长的数据亟需自动化的数据处理技术,深度学习(DL)技术恰好可以帮我们解决这个问题。由于近些年材料科学的DL技术发展迅速,我们急需一个综述来梳理该领域的最近研究。


来自美国国家标准与技术研究院的Kamal Choudhary团队讨论了DL方法中的基本原理,重点介绍了DL应用于材料科学的最新进展中的主要趋势,并且提供了一个可更新的公开github库以囊括最新的DL工具及数据库。该综述不仅高度概括了深度学习方法,还简要介绍了DL领域一些重要方法的基本概念,列举了很多DL在材料科学领域的最新应用进展,总结了DL的局限性和现阶段面临的挑战。这项综述工作对深度学习技术及材料科学的发展具有重要的参考价值与指导意义。

该文近期发表于npj Computational Materials 8:59 (2022),英文标题与摘要如下,点击左下角“阅读原文”可以自由获取论文PDF。

Recent advances and applications of deep learning methods in materials science

Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol Woo Park, Alok Choudhary, Ankit Agrawal, Simon J. L. Billinge, Elizabeth Holm, Shyue Ping Ong & Chris Wolverton

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.


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