标题： 开放催化剂实验2024（OCx24）：连接实验与计算模型 显示英文标题

标题： Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models

摘要： 寻找低成本、耐用且有效的催化剂对于绿色氢气生产和二氧化碳升级以帮助缓解气候变化至关重要。 目前，新催化剂的发现受到人工智能加速计算模型预测与实验研究结果之间差距的限制。 为了取得进展，需要大量且多样的实验数据集，这些数据集应可重复并在工业相关条件下进行测试。 我们通过利用全面的高通量表征和实验流程来创建开放催化剂实验2024（OCX24）数据集，以满足这些需求。 该数据集包含使用湿法和干法合成的572个样品，并进行了X射线荧光和X射线衍射表征。 我们制备了441个气体扩散电极，包括重复样本，并在电流密度高达$300$mA/cm$^2$的条件下，使用零间隙电解法对它们进行了二氧化碳还原反应（CO$_2$RR）和析氢反应（HER）的评估。 为了找到与实验结果的相关性并进行计算筛选，在$\sim$20,000种无机材料上计算了六种吸附物的DFT验证吸附能，这需要6.85亿次AI加速的弛豫。 值得注意的是，从这一大组材料中，基于数据的萨巴蒂尔火山图独立地将Pt识别为HER的顶级候选材料，而没有对Pt或Pt合金样品进行任何实验测量。 我们预计专门为AI训练生成的实验数据的可用性，如OCX24，将显著提高计算模型在选择用于实验筛选的材料方面的实用性。 显示英文摘要

摘要： The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Discovery of new catalysts is currently limited by the gap between what AI-accelerated computational models predict and what experimental studies produce. To make progress, large and diverse experimental datasets are needed that are reproducible and tested at industrially-relevant conditions. We address these needs by utilizing a comprehensive high-throughput characterization and experimental pipeline to create the Open Catalyst Experiments 2024 (OCX24) dataset. The dataset contains 572 samples synthesized using both wet and dry methods with X-ray fluorescence and X-ray diffraction characterization. We prepared 441 gas diffusion electrodes, including replicates, and evaluated them using zero-gap electrolysis for carbon dioxide reduction (CO$_2$RR) and hydrogen evolution reactions (HER) at current densities up to $300$ mA/cm$^2$. To find correlations with experimental outcomes and to perform computational screens, DFT-verified adsorption energies for six adsorbates were calculated on $\sim$20,000 inorganic materials requiring 685 million AI-accelerated relaxations. Remarkably from this large set of materials, a data driven Sabatier volcano independently identified Pt as being a top candidate for HER without having any experimental measurements on Pt or Pt-alloy samples. We anticipate the availability of experimental data generated specifically for AI training, such as OCX24, will significantly improve the utility of computational models in selecting materials for experimental screening.