Recent advances and applications of deep learning methods in materials science

Abstract 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…

• NS
  National Science Foundation

  Awards: CMMI-1826218, DMREF-1922234, 2053929, 1826218, 1922234, CMMI-2053929
• UD
  U.S. Department of Energy

  Awards: 70NANB19H005, KC23MP, DE-AC02-05-CH11231: Materials Project program KC23MP, -AC02-05-CH11231, DE-AC02, AC02-05-CH11231, DE-AC02-05-CH11231, DE-AC02-
• UD
  U.S. Department of Commerce

  Awards: 70NANB19H005, 70NANB19H117
• NI
  National Institute of Standards and Technology

  Awards: 70NANB19H117, 70NANB19H005
• OO
  Office of Science

  Awards: DE-AC02, DE-AC02-05-CH11231 : Materials Project program KC23MP, DE-AC02-05-CH11231
• DO
  Division of Civil, Mechanical and Manufacturing Innovation

  Award: CMMI-2053929
• BE
  Basic Energy Sciences

  Awards: DE-AC02, AC02-05-CH11231, KC23MP, DE-AC02-05-CH11231