机器学习辅助的过渡金属碳量子点析氢催化剂设计与优化
Machine Learning Assisted Design and Optimization of Transition Metal-Incorporated Carbon Quantum Dot Catalysts for Hydrogen Evolution Reaction
摘要：
开发具有优异催化活性的低成本析氢反应（HER）催化剂，取代成本高昂的贵金属基催化剂，是实现绿色制氢的关键。促进无贵金属催化剂的催化性能的流行策略是将地球上丰富的过渡金属（TM）原子掺入纳米碳平台如碳量子点（CQD）中。尽管数据驱动的催化剂设计方法可以显著加速TM元素掺杂的CQD（M@CQD）催化剂的合理设计，但是它们受到简化的理论模型或过高的成本和实验数据生成的复杂性的影响。在这项研究中，我们提出了一个有效的和简便的HER催化剂设计策略的基础上，机器学习（ML）和ML模型验证使用电化学方法伴随着密度泛函理论（DFT）模拟。基于贝叶斯遗传算法（BGA）ML模型，在优化催化剂负载量、电极类型、电解液温度和pH值的条件下，提出了三维还原氧化石墨烯（3D rGO）导体上的Ni@CQD催化剂为最佳HER催化剂.我们用电化学实验验证了ML结果，其中Ni@CQD催化剂表现出优异的HER活性，需要189 mV的过电位以实现10 mA cm-2，Tafel斜率为52 mV dec-1，并且在酸性介质中具有令人印象深刻的耐久性。我们期望这种方法和Ni@CQD催化剂的优异性能为合理设计用于商业应用的高活性电催化剂提供有效途径。
英文摘要：
Development of cost-effective hydrogen evolution reaction (HER) catalysts with outstanding catalytic activity, replacing cost-prohibitive noble metal-based catalysts, is critical for practical green hydrogen production. A popular strategy for promoting the catalytic performance of noble metal-free catalysts is to incorporate earth-abundant transition metal (TM) atoms into nanocarbon platforms such as carbon quantum dots (CQDs). Although data-driven catalyst design methods can significantly accelerate the rational design of TM element-doped CQD (M@CQD) catalysts, they suffer from either a simplified theoretical model or the prohibitive cost and complexity of experimental data generation. In this study, we propose an effective and facile HER catalyst design strategy based on machine learning (ML) and ML model verification using electrochemical methods accompanied with density functional theory (DFT) simulations. Based on a Bayesian genetic algorithm (BGA) ML model, the Ni@CQD catalyst on a three-dimensional reduced graphene oxide (3D rGO) conductor is proposed as the best HER catalyst under the optimal conditions of catalyst loading, electrode type, and temperature and pH of electrolyte. We validate the ML results with electrochemical experiments, where the Ni@CQD catalyst exhibited superior HER activity, requiring an overpotential of 189 mV to achieve 10 mA cm-2 with a Tafel slope of 52 mV dec-1 and impressive durability in acidic media. We expect that this methodology and the excellent performance of the Ni@CQD catalyst provide an effective route for the rational design of highly active electrocatalysts for commercial applications.
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