在多阶段机器学习驱动的先进酸性析氧电催化剂高效发现方法中利用数据挖掘、主动学习与域适应
Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts
AI总结：
本研究提出整合数据挖掘、主动学习与域适应的多阶段机器学习方法，系统缩小探索空间并迭代优化，高效发现新型Ru-Mn-Ca-Pr氧化物酸性OER催化剂，为电催化剂研发提供数据驱动新范式。
摘要：
开发先进的酸性析氧反应（OER）催化剂对可持续制氢至关重要。本研究提出一种新颖的多阶段机器学习（ML）方法，以简化复杂多金属催化剂的发现与优化流程。我们的方法在整个材料发现过程中整合了数据挖掘、主动学习和域适应技术。与传统试错法不同，该方法利用领域知识系统缩小探索空间，最大限度减少对主观直觉的依赖。随后，主动学习模块通过迭代实验反馈高效优化元素组成与合成条件。该流程最终发现了一种极具潜力的Ru-Mn-Ca-Pr氧化物催化剂。我们的工作流还通过域适应策略增强了理论模拟，提供了与实验结果一致的更深层次机理解释。通过利用多源数据与多种机器学习策略，我们为电催化剂的发现与优化建立了一条高效路径。这种全面的数据驱动方法代表了电催化剂研究的范式转变，并有望成为新的基准。
英文摘要：
Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study introduces a novel, multi-stage machine learning (ML) approach to streamline the discovery and optimization of complex multi-metallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we establish an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially new benchmark in electrocatalysts research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
