SAA上CH解离数据库

单原子合金 （SAA） 在催化方面具有重要前景，特别是在促进 C-H 键解离方面，C-H 键解离是制氢和碳氢化合物加工的关键反应。这些材料因其卓越的选择性和效率而备受推崇，对于推进清洁能源解决方案和改进化学工艺至关重要。<br>本研究介绍了一个精心策划的数据集，其中包含超过 10,950 个条目，这些条目来自结合第一性原理密度泛函理论 （DFT） 和高级机器学习 （ML） 技术的混合方法。最初，对 600 多种 SAA 成分进行了 DFT 计算，以捕获基本相互作用和能量景观。随后，这些DFT结果被用于训练机器学习模型，然后预测了超过10,000个SAA的C-H解离能垒，从而提供了对其催化行为的全面见解。<br>特别是，该数据集侧重于顶级站点反应，其中超过 8,638 个条目显示了拟合良好的 ML 预测，表明这些配置中的模型可靠性很强。该数据集不仅提供了表面能和电子特性等详细性质，还为SAAs的理论探索开辟了新的途径。DFT数据与ML预测的融合丰富了数据集，使其成为旨在开发新型催化剂和探索新催化工艺的研究人员的宝贵资源，最终推动了材料科学和催化的界限。

Single-atom alloys (SAAs) hold significant promise in catalysis, particularly in facilitating C-H bond dissociation—a critical reaction in hydrogen production and hydrocarbon processing. These materials are highly regarded for their exceptional selectivity and efficiency, and are crucial for advancing clean energy solutions and improving chemical manufacturing processes.
This study presents a curated dataset containing over 10,950 entries derived from a hybrid approach combining first-principles density functional theory (DFT) and advanced machine learning (ML) techniques. Initially, DFT calculations were performed on over 600 SAA compositions to capture fundamental interactions and energy landscapes. Subsequently, these DFT results were used to train machine learning models, which were then employed to predict C-H dissociation energy barriers for over 10,000 SAAs, providing comprehensive insights into their catalytic behavior.
Specifically, this dataset focuses on top-site reactions, with over 8,638 entries exhibiting well-fitted ML predictions, demonstrating strong model reliability for these configurations. In addition to providing detailed properties such as surface energy and electronic characteristics, this dataset also opens new avenues for theoretical investigations of SAAs. The integration of DFT data and ML predictions enriches this dataset, making it a valuable resource for researchers aiming to develop novel catalysts and explore new catalytic processes, ultimately pushing the boundaries of materials science and catalysis.