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Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

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journal contribution
posted on 2021-04-13, 14:53 authored by Zhiqiang Niu, Valerie PinfieldValerie Pinfield, Billy Wu, Huizhi Wang, Kui Jiao, Dennis YC Leung, Jin Xuan

Porous energy materials are essential components of many energy devices and systems, the development of which have been long plagued by two main challenges. The first is the ‘curse of dimensionality’, i.e. the complex structure–property relationships of energy materials are largely determined by a highdimensional parameter space. The second challenge is the low efficiency of optimisation/discovery techniques for new energy materials. Digitalisation of porous energy materials is currently being considered as one of the most promising solutions to tackle these issues by transforming all material information into the digital space using reconstruction and imaging data and fusing this with various computational methods. With the help of material digitalisation, the rapid characterisation, the prediction of properties, and the autonomous optimisation of new energy materials can be achieved by using advanced mathematical algorithms. In this paper, we review the evolution of these computational and digital approaches and their typical applications in studying various porous energy materials and devices. Particularly, we address the recent progress of artificial intelligence (AI) in porous energy materials and highlight the successful application of several deep learning methods in microstructural reconstruction and generation, property prediction, and the performance optimisation of energy materials in service. We also provide a perspective on the potential of deep learning methods in achieving autonomous optimisation and discovery of new porous energy materials based on advanced computational modelling and AI techniques.

Funding

EP/S003053/1 grant number FIRG003

EP/S000933/1

EP/V011863/1

Royal Society K.C. Wong International Fellowship (NIF\R1\191864)

National Natural Science Foundation of China (51861130359)

Faraday Institution Multi-Scale Modelling project

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Chemical Engineering

Published in

Energy & Environmental Science

Volume

14

Issue

5

Pages

2549-2576

Publisher

Royal Society of Chemistry (RSC)

Version

  • VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by RSC under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

2021-04-01

Publication date

2021-04-01

Copyright date

2021

ISSN

1754-5692

eISSN

1754-5706

Language

  • en

Depositor

Dr Valerie Pinfield. Deposit date: 12 April 2021

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