Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

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42 Scopus citations

Abstract

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent space, and through exploration and manipulation of the latent space can generate new materials. These approaches learn representations of a material structure and its corresponding chemistry or physics to accelerate materials discovery, which differs from traditional AI/ML methods that use statistical and combinatorial screening of existing materials via distinct structure-property relationships. However, application of GMs to inorganic materials has been notably harder than organic molecules because inorganic structure is often more complex to encode. In this work we review recent innovations that have enabled GMs to accelerate inorganic materials discovery. We focus on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation.

Original languageEnglish
Article number865270
JournalFrontiers in Materials
Volume9
DOIs
StatePublished - Mar 22 2022

Funding

Center for Nanophase Materials Sciences (CNMS) and the Alvin M. Weinberg Fellowship at Oak Ridge National Laboratory. AF acknowledges support from the Alvin M. Weinberg Fellowship at Oak Ridge National Laboratory. This work was carried out at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences, a US Department of Energy Office of Science User Facility.

Keywords

  • artificial intelligence
  • deep learning
  • generative adversarial networks
  • generative models
  • inverse design
  • machine learning
  • materials discovery
  • variational autoencoders

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