Machine learning has proven a powerful tool for accelerating the computational characterization of energy materials. The neural-network approach by Lu et al. allows bypassing time-consuming first principles calculations for the design of catalysts based on high-entropy alloys. This work is an example of a growing number of case studies identifying descriptors of catalytic performance using machine learning instead of physical intuition.

中文翻译：

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学习什么使催化剂好

事实证明，机器学习是加速能源材料的计算表征的强大工具。Lu等人的神经网络方法。允许绕开基于高熵合金的催化剂设计所需的费时的第一性原理计算。这项工作是一个越来越多的案例研究的例子，这些案例研究使用机器学习而非物理直觉来识别催化性能的描述符。