Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning

Abstract

(1 of 25)development, it is still far from meeting the increased demand. [3] The emergence of artificial intelligence (AI) has fundamentally changed the situation, which has significantly accelerated the discovery process, owing to greatly improved algorithms and developments in data science. [4] Machine learning (ML), a simple and practical AI framework based on computer and statistical science, is used to develop algorithms to learn from historic data without being explicitly programmed to obtain specific results. [5] It can investigate relationships that are hard to clearly and definitely model mathematically, providing insights for new scientific advancements related to highly complex with many uncertain twisted together factors. [6] There are usually three factors that govern the learning and prediction process of an ML: algorithms, data/database, and descriptors. [4] The algorithms involve data extraction, data filing, and propagation from mathematical derivation. [5,7] The data can be derived not only from experiments but also from theoretical calculations. [4] A number of databases based on experiments [8] and calculations [9] have already been established. The descriptors depend to a large extent on the predicted material or properties. Based on the algorithm, databases, and descriptors, the ML applications have been successfully implemented to support various energy materials with analysis tools (e.g., Python-based SciKit-Learn [10] and TensorFlow [11] ) in combination with workflow management tools (e.g., ASE [12] and Atomate [13] ). However, the prediction accuracy depends highly on the descriptors, as descriptors have a certain uniqueness for various materials and properties as long as the algorithm is selected correctly and the data set is complete. [14] For catalysis, the descriptors contain the essence from the physicochemical nature. Based on effective descriptors, ML can uncover the relationship bridging structure and its activity, selectivity, and stability. [5,15] Thus, suitable descriptors must be established to understand the structure-activity relationship. Although many efforts have been made to accelerate the rational design of homogeneous catalysts, [7b,16] heterogeneous catalysts, [7b,16b,17] and electrocatalysis, [3,18] the development of ML-assisted real catalysts is still in its infancy. Despite these considerable research efforts, the lack of universal selection tactics for descriptors bridging the gap between activity and structures impedes the application

N-doped graphene for electrocatalytic O2 and CO2 reduction

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Multi-atom cluster catalysts for efficient electrocatalysis

“…Besides, the use of machine learning has also been a hot topic recently, and it will be interesting to direct some efforts in combining the concept of multi-atom cluster catalysts with machine learning to achieve more interesting progress. 262 In that case, the combination of geometrical and electronic Chem. Soc.…”

Advanced Materials for Thin‐Film Solid Oxide Fuel Cells: Recent Progress and Challenges in Boosting the Device Performance at Low Temperatures

“…In this regard, the emerging ML offers some hope through an accelerated process to have a breakthrough on the materials side. [237,238] As a first step, the material design strategy should begin from theoretical insights identifying effective descriptors such as "the oxygen p-band theory," [66,239] degree of e g -filling on the transition metals in the electrode, [97] or acidity of dopants (compared to host material). [240] Next, based on these descriptors, DFT studies are desired for proposing novel materials with specific elements and structures.…”