Machine learning: Accelerating materials development for energy storage and conversion

An Chen

doi:10.1002/inf2.12094

2020-02-23

240

An Chen

张旭 (Xu Zhang)

周震 (Zhen Zhou) *

DOI：10.1002/inf2.12094

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摘要

En 中文

With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery. In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energy-related fields, including catalysis, batteries, solar cells, and gas capture. Moreover, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science.

Keyword:

big data

energy storage and conversion

machine learning

property prediction

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期刊

InfoMat

IF:

22.3

论文数:

343

被引数:

8.2K

机构

Nankai University

学者数:

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论文数: 560

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