水准点(测量)
吞吐量
催化作用
计算机科学
还原(数学)
纳米技术
工艺工程
化学
材料科学
工程类
数学
有机化学
无线
几何学
地理
电信
大地测量学
作者
Wilton J. M. Kort-Kamp,Magali Ferrandon,Xiaoping Wang,Jae Hyung Park,Rajesh K. Malla,Towfiq Ahmed,Edward F. Holby,Deborah J. Myers,Piotr Zelenay
标识
DOI:10.1016/j.jpowsour.2022.232583
摘要
Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.
科研通智能强力驱动
Strongly Powered by AbleSci AI