Review Articles in 2023

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  • Autoencoders are versatile tools for molecular informatics with the opportunity for advancing molecule and drug design. In this Review, the authors highlight the active areas of development in the field and explore the challenges that need to be addressed moving forward.

    • Agnieszka Ilnicka
    • Gisbert Schneider
    Review Article
  • The capability of predicting stable materials is important to further accelerate the discovery of novel materials. In this Review, the authors discuss recent developments in machine learning techniques for assessing the stability of materials and highlight the opportunities in further advancing the field.

    • Sean D. Griesemer
    • Yi Xia
    • Chris Wolverton
    Review Article
  • The computational characterization of short-range order in compositionally complex materials relies on effective interatomic potentials. In this Review, challenges and opportunities in developing advanced potentials for such systems are discussed, with a focus on machine learning-based potentials.

    • Alberto Ferrari
    • Fritz Körmann
    • Jörg Neugebauer
    Review Article