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Home » Institute » Departments & Research Groups » Artificial Intelligence for Integrated Material Science » AIMS Publications

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  • X. Fu, X. Guo, P. Shi et al. Control of CO2 electrocatalysis via modularly customizable graphdiyne. Journal of the American Chemical Society, X, X, (2025)
  • K. Gao, W. Cui, T. Cerqueira et al. Enhanced superconductivity in X4H15 compounds via hole-doping at ambient pressure. Advanced Science, 12, e08419, (2025)
  • J. Jacobs, C. Ritter, Ke. Xu et al. Structural and electronic tunability of Ruddlesden–Popper oxyfluorides through nickel–copper substitution in La2Ni1−xCuxO2.5F3 (0 ≤ x ≤ 1). Journal of Materials Chemistry A, 13, 32539–32550, (2025)
  • T. da Silva, T. Cerqueira, H. Wang et al. High-throughput study of kagome compounds in the AV3Sb5 family. Digital Discovery, 4, 2431–2438, (2025)
  • K. Xie, Ye. Shen, L. Lin et al. Machine learning-enhanced design of 2D TM3(HXBHYB)@MOF-based single-atom catalysts for efficient oxygen electrocatalysis. The Journal of Physical Chemistry Letters, 16, 9682, (2025)
  • Q. Wang, X. Guo, Mo. Lin et al. Protonation and deprotonation of edges in graphene oxide and MXenes as a driving force for actuation in responsive 2D membranes. Nature Communications, 16, 8683, (2025)
  • K. Gao, T. Cerqueira, A. Sanna et al. The maximum Tc of conventional superconductors at ambient pressure. Nature Communications, 16, 8253, (2025)
  • W. Wang, X. Guo, Y. Wang et al. Transformation of CO2 to C2+ alcohols by tailoring the oxygen bonding via Fe-based tandem catalyst. Nature Communications, 16, 7265, (2025)
  • T. da Silva, T. Cavignac, T. Cerqueira et al. Machine-learning accelerated prediction of two-dimensional conventional superconductors. Materials Horizons, -, -, (2025)
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