论文标题
通过预测和高通量实验发现复杂的固体溶液电催化剂发现
Complex solid solution electrocatalyst discovery by prediction and high-throughput experimentation
论文作者
论文摘要
有效发现电化学能量转化反应的电催化剂对于打击气候变化至关重要。以氧还原反应的示例，我们表明，通过使用数据驱动的发现周期，可以掌握由组成复杂的固体溶液（高熵合金）电催化剂提供的多维性挑战。迭代精致的计算模型预测了Quinary目标组成的活动趋势，该Quinary构成围绕着连续的成分扩散薄膜文库。然后输入高通量表征数据集以进行模型的细化。精制模型正确预测了用于氧还原反应的模型模型系统AG-IR-PD-PT-RU的活性最大值。该方法可以以前所未有的方式识别电化学反应的最佳复杂固溶。
Efficient discovery of electrocatalysts for electrochemical energy conversion reactions is of utmost importance to combat climate change. With the example of the oxygen reduction reaction we show that by utilising a data-driven discovery cycle, the multidimensionality challenge offered by compositionally complex solid solution (high entropy alloy) electrocatalysts can be mastered. Iteratively refined computational models predict activity trends for quinary target compositions, around which continuous composition spread thin-film libraries are synthesized. High-throughput characterisation datasets are then input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru for the oxygen reduction reaction. The method can identify optimal complex solid solutions for electrochemical reactions in an unprecedented manner.
