通过将机器学习与多尺度模拟相结合,识别CO2减少的活跃地点
Yalu Chen1, Yufeng Huang1, Tao Cheng1,2
1Materials and Process Simulation Center (MSC) and Joint Center for Artificial Photosynthesis (JCAP) , California Institute of Technology , Pasadena , California 91125 , United States.
Journal of the American Chemical Society
|July 10, 2019
概括
研究人员使用机器学习和模拟来识别黄金纳米颗粒上的活性位点,用于将二氧化碳转化为一氧化碳. 这种方法有效地确定了在清洁能源应用中改善电催化剂设计的最佳地点.
科学领域:
- 计算材料科学
- 电催化
- 纳米技术
背景情况:
- 黄金纳米粒子 (AuNPs) 和非合的Au3Fe核心外纳米粒子在降低二氧化碳到二氧化碳 (CO2RR) 中表现出增强的性能.
- 由于表面原子的数量众多以及传统实验和量子力学方法的局限性,鉴定导致这种 CO2RR 性能改善的特定活性表面位点是具有挑战性的.
- 目前的方法难以分析纳米颗粒的庞大表面积 (例如,10纳米NP的表面积约为10,000个).
研究的目的:
- 开发和应用结合机器学习,多尺度模拟和量子力学 (QM) 的计算方法,以预测 AuNP 和 dealloyed Au 表面上个体表面的催化性能.
- 在非合金表面有效地确定CO2RR的最佳活性位点.
- 为整个纳米粒子表面提供可视化催化活动的工具.
主要方法:
- 机器学习算法的集成与多尺度模拟和QM计算.
- 预测金纳米颗粒和非合金表面上数千个表面位置的性能 (a值).
- 通过计算分析确定最佳的二氧化碳减排反应 (CO2RR) 活性位点.
主要成果:
- 成功预测了金纳米粒子和非合金金表面的单个表面位置的性能.
- 在没有合金的黄金表面上确定了CO2RR的最佳活性位点,计算成本大大降低.
- 开发了一种可视化整个纳米粒子表面的催化活性分布的方法.
结论:
- 机器学习,多尺度模拟和质量管理的结合方法是理解和预测现场电催化剂性能的一种强大工具.
- 这种方法可以有效地确定CO2RR的最佳活性位点,加速高性能电催化剂的设计.
- 将预测的a值与实验或理论描述符进行比较为指导开发用于清洁能源转换的先进电催化剂提供了新的途径.
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