构建6000万条数据的吸附材料数据库，助力二氧化碳捕集材料发现
The Open DAC 2025 Dataset for Sorbent Discovery in Direct Air Capture
- 基于6000万次密度泛函计算，涵盖15000种金属有机框架材料
- 引入功能化与高能构型，显著提升材料多样性与模拟精度
- 配套先进机器学习势函数，可高效预测吸附性能，适合材料设计者使用
从湿空气中识别适用于直接空气捕集（DAC）的有效吸附材料仍具挑战。本文发布开放的ODAC2025（ODAC25）数据集，是对先前ODAC23（Sriram et al., ACS Central Science, 10 (2024) 923）的重大扩展与改进，包含近6000万次针对CO₂、H₂O、N₂和O₂在15,000种金属有机框架（MOFs）中的密度泛函理论（DFT）单点计算。ODAC25通过功能化MOFs、高能分子动力学衍生构型及合成生成框架，显著提升了化学与构型多样性。同时，该数据集大幅改善了DFT计算精度与柔性MOFs的处理方式。随数据集发布，我们还提供了基于ODAC25训练的新型最先进的机器学习原子间势函数，并在吸附能与亨利系数预测上进行了评估。
原文摘要 · Abstract (English)
Identifying useful sorbent materials for direct air capture (DAC) from humid air remains a challenge. We present the Open DAC 2025 (ODAC25) dataset, a significant expansion and improvement upon ODAC23 (Sriram et al., ACS Central Science, 10 (2024) 923), comprising nearly 60 million DFT single-point calculations for CO$_2$, H$_2$O, N$_2$, and O$_2$ adsorption in 15,000 MOFs. ODAC25 introduces chemical and configurational diversity through functionalized MOFs, high-energy GCMC-derived placements, and synthetically generated frameworks. ODAC25 also significantly improves upon the accuracy of DFT calculations and the treatment of flexible MOFs in ODAC23. Along with the dataset, we release new state-of-the-art machine-learned interatomic potentials trained on ODAC25 and evaluate them on adsorption energy and Henry's law coefficient predictions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。