Accelerating Optimizing the Design of Carbon‐based Electrocatalyst via Machine Learning

Abstract

In this era of artificial intelligence, we urgently want to optimize the current material design methods to come up with a more efficient and more accurate closedloop system. The approach requires at least three parts including high-throughput screening, automated synthesis platform, and machine learning algorithms. Fortunately, the techniques mentioned above have been substantial developed. We have introduced the common algorithms of machine learning. Then, several machine learningbased design of carbon-based electrocatalysts are discussed. We tried to illustrate the research norms involving machine learning. Besides, other paper structures and details have been also discussed.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

“…As mentioned, because PCA retains all original descriptors, models trained on principal components lose performance when many of the original descriptors are uninformative (noise). PCA is commonly employed in modeling of materials properties where a large number of features have been generated, such as heterogeneous catalysts, photovoltaics, and supramolecular materials. ,,, Given that PCA only builds linear projections but structure–property relationships are often nonlinear, additional learning algorithms have been developed to perform nonlinear dimension reduction. Kernel PCA uses standard PCA to perform nonlinear dimensional reduction by using a nonlinear transformation to project the data points into a higher dimension feature space where standard PCA can be used .…”

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

“…Although PCA is frequently used to model the properties of materials from a large number of descriptors (features), it only creates linear projections, whereas the correlations between structure and property and between property and performance are typically nonlinear in nature. 214 Thus, additional methods have been created to perform nonlinear dimension reduction functions considering the challenge at hand. Kernel PCA, which uses standard PCA to carry out nonlinear dimension reduction, was created to manage the difficulties of the nonlinearity nature of structure-property and property-performance correlations in electrocatalysis.…”

Tuning OER Electrocatalysts toward LOM Pathway through the Lens of Multi-Descriptor Feature Selection by Artificial Intelligence-Based Approach

“…Although PCA is commonly employed in modeling materials or electrocatalysts properties from large number of descriptors (features), it only builds linear projections. On the other hand, structure–property or electronic–property relationships are most often nonlinear in nature . Therefore, additional algorithms have been developed to execute function on nonlinear dimension reduction.…”

“…On the other hand, structure−property or electronic−property relationships are most often nonlinear in nature. 136 Therefore, additional algorithms have been developed to execute function on nonlinear dimension reduction. Kernel PCA was developed to handle the hurdles of nonlinearity nature of structure−property relationship and uses standard PCA to execute nonlinear dimension reduction.…”