用于电催化剂设计的Catalyst GFlowNet：析氢反应案例研究
Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
AI总结：
针对氢能存储中低成本高性能电催化剂开发难题，提出基于机器学习能预测的Catalyst GFlowNet生成模型，经析氢反应验证可识别铂为最优催化剂，为加速新型高效催化剂研发提供新路径。
摘要：
高效且低成本的储能对于加速可再生能源的普及、确保能源供应稳定至关重要，可应对风能、太阳能等能源的波动性问题。电催化剂在氢能存储（HES）中发挥着关键作用，可将能量以氢的形式储存。然而，开发适用于该过程的低成本高性能催化剂仍是一项重大挑战。我们提出了Catalyst GFlowNet，这是一种生成模型，利用基于机器学习的形成能与吸附能预测器来设计可作为高效催化剂的晶体表面。我们通过将该模型概念验证应用于氢能存储中的关键反应——析氢反应，验证了其性能，研究中我们成功识别出铂是已知最高效的催化剂。在未来工作中，我们计划将该方法拓展至析氧反应（目前该反应的最优催化剂为昂贵的金属氧化物），并扩大搜索空间以发现新材料。该生成建模框架为加速寻找新型高效催化剂提供了一条颇具前景的路径。
英文摘要：
Efficient and inexpensive energy storage is essential for accelerating the adoption of renewable energy and ensuring a stable supply, despite fluctuations in sources such as wind and solar. Electrocatalysts play a key role in hydrogen energy storage (HES), allowing the energy to be stored as hydrogen. However, the development of affordable and high-performance catalysts for this process remains a significant challenge. We introduce Catalyst GFlowNet, a generative model that leverages machine learning-based predictors of formation and adsorption energy to design crystal surfaces that act as efficient catalysts. We demonstrate the performance of the model through a proof-of-concept application to the hydrogen evolution reaction, a key reaction in HES, for which we successfully identified platinum as the most efficient known catalyst. In future work, we aim to extend this approach to the oxygen evolution reaction, where current optimal catalysts are expensive metal oxides, and open the search space to discover new materials. This generative modeling framework offers a promising pathway for accelerating the search for novel and efficient catalysts.
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