September 2023 Issue

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Deng, B., Zhong, P., Jun, K. et al. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

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  • Despite their efficiency advantages, the performance of photonic neural networks is hampered by the accumulation of inherent systematic errors. Zheng et al. propose a dual backpropagation training approach, which allows the network to adapt to systematic errors, thus outperforming state-of-the-art in situ training approaches.

    • Ziyang Zheng
    • Zhengyang Duan
    • Xing Lin
  • Local methods of explainable artificial intelligence identify where important features or inputs occur, while global methods try to understand what features or concepts have been learned by a model. The authors propose a concept-level explanation method that bridges the local and global perspectives, enabling more comprehensive and human-understandable explanations.

    • Reduan Achtibat
    • Maximilian Dreyer
    • Sebastian Lapuschkin
    ArticleOpen Access
  • An outstanding challenge in materials science is doing large-scale simulations with complex electron interactions. Deng and colleagues introduce a universal graph neural network-based interatomic potential integrating atomic magnetic moments as charge constraints, which allows for capturing subtle chemical properties in several lithium-based solid-state materials

    • Bowen Deng
    • Peichen Zhong
    • Gerbrand Ceder
    ArticleOpen Access
  • For virtual protein docking, an accurate scoring function is necessary that evaluates how likely a protein conformation is. Stebliankin and colleagues present a method based on vision transformers that provides a more accurate score by evaluating individual binding interfaces as multi-channel images.

    • Vitalii Stebliankin
    • Azam Shirali
    • Giri Narasimhan