用生成模型设计高效廉价的制氢催化剂，成功找到铂最优。
Catalyst GFlowNet for electrocatalyst design: A hydrogen evolution reaction case study
- 基于机器学习预测能量，生成新型晶体表面催化剂。
- 在析氢反应中验证，铂被确认为最高效已知催化剂。
- 适合材料设计与新能源领域研究者参考。
高效且低成本的能量存储对加速可再生能源（如风能、太阳能）的采用及保障稳定供应至关重要。电催化剂在氢储能（HES）中起关键作用，可将能量以氢气形式储存。然而，开发成本低且性能高的催化剂仍面临重大挑战。本文提出Catalyst GFlowNet，一种生成模型，利用基于机器学习的形成能和吸附能预测器，设计出高效的晶体表面催化剂。通过析氢反应（HER）这一氢储能关键反应的案例研究，验证了该模型的有效性，成功识别出铂为目前已知最高效的催化剂。未来工作将扩展至氧析出反应，当前最优催化剂为昂贵金属氧化物，同时扩大材料搜索空间以发现新物质。该生成建模框架为加速新型高效催化剂的发现提供了有前景的路径。
原文摘要 · Abstract (English)
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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