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文章出处： https://doi.org/10.1016/j.matt.2024.10.001 （点击文末「阅读原文」，直达链接） 通讯作者及单位： Pengfei Ou, Edward H. Sargent – Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON M5S 1A4, Canada; Department of Chemistry, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA. Isaac Tamblyn - Department of Physics, University of Ottawa, 150 Louis-Pasteur Private, Ottawa, ON K1N 6N5, Canada; Department of Physics, University of Ottawa, 150 Louis-Pasteur Private, Ottawa, ON K1N 6N5, Canada. 摘要 密度泛函理 (DFT) 通过化学中间体在预期吸附位点的能量来预测电催化反应的过电势，帮助筛选新型电催化剂。我们提出，在训练基于DFT数据的机器学习模型时，引入吸附位点之间相似性的定量指标能够提升模型的预测准确性。通过将相似性指标作为输入特征加入基于图神经网络的机器学习
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