积极学习指导发现了一个冠军的四金属矿氧化物,用于氧化物进化电催化
Junseok Moon1,2, Wiktor Beker3,4, Marta Siek5
1Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea.
Nature materials
|November 3, 2023
概括
使用有限的数据和结构信息进行主动学习,成功地确定了用于氧化演化反应的高性能氧化物电催化剂,克服了之前的机器学习限制.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 催化剂是一种催化剂.
背景情况:
- 矿氧化物是氧化演化反应 (OER) 的有希望的电催化剂.
- 用于催化剂发现的机器学习 (ML) 被有限的实验数据所阻碍.
- 目前的催化剂发现严重依赖于由人类驱动的实验活动.
研究的目的:
- 为了证明积极学习,即使是小数据集,也可以发现高性能电催化剂.
- 为了克服ML驱动的材料发现中的数据稀缺性限制.
- 为有效的OER确定新的矿氧化物化合物.
主要方法:
- 利用了主动学习与信息化的结构性特征化数据相结合.
- 实施了一个闭环实验方法.
- 在一个小的,补充数据集上训练的ML模型.
主要成果:
- 开发的ML模型成功地复制了矿氧化物的已知实验趋势.
- 确定了一种新的四金属矿氧化物组成.
- 在OER的10 mA cm-2时达到391 mV的内在超电位,是四金属矿中报告的最低值之一.
结论:
- 使用有限,增强数据的积极学习是发现先进电催化剂的可行策略.
- 这种方法可以加快能源应用材料的发现.
- 鉴定到的矿氧化物代表了OER催化剂性能的显著进步.
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