Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts

IF 7.9 2区 化学 Q1 CHEMISTRY, PHYSICAL
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引用次数: 0

Abstract

The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. We review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.

Abstract Image

机器学习指导下的原子分散电催化剂设计、合成和表征
最近,机器学习与材料设计的结合彻底改变了人们对结构-性能关系的理解,并使材料性能的优化超越了试错模式。一方面,机器学习大大加快了原子分散金属-氮-碳(M-N-C)电催化剂的开发速度,而传统的电催化剂主要依赖启发式方法。另一方面,利用机器学习加速 M-N-C 材料发现的主要挑战在于与数据收集相关的成本。我们回顾了最近用于 M-N-C 催化剂开发的机器学习集成策略,包括讨论理论设计、通过主动学习优化合成以及高级显微表征所采用的符号回归和卷积神经网络等典型算法。随后,我们就机器学习辅助开发新型 M-N-C 催化剂的近期潜在方向提出了自己的观点,并阐明了这一类材料的选择性、活性和耐久性的复杂物理化学机制。
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来源期刊
Current Opinion in Electrochemistry
Current Opinion in Electrochemistry Chemistry-Analytical Chemistry
CiteScore
14.00
自引率
5.90%
发文量
272
审稿时长
73 days
期刊介绍: The development of the Current Opinion journals stemmed from the acknowledgment of the growing challenge for specialists to stay abreast of the expanding volume of information within their field. In Current Opinion in Electrochemistry, they help the reader by providing in a systematic manner: 1.The views of experts on current advances in electrochemistry in a clear and readable form. 2.Evaluations of the most interesting papers, annotated by experts, from the great wealth of original publications. In the realm of electrochemistry, the subject is divided into 12 themed sections, with each section undergoing an annual review cycle: • Bioelectrochemistry • Electrocatalysis • Electrochemical Materials and Engineering • Energy Storage: Batteries and Supercapacitors • Energy Transformation • Environmental Electrochemistry • Fundamental & Theoretical Electrochemistry • Innovative Methods in Electrochemistry • Organic & Molecular Electrochemistry • Physical & Nano-Electrochemistry • Sensors & Bio-sensors •
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