Machine Learning Assisted Molecule Design of Fuel

Zhang Xiangwen, Hou Fang, Liu Ruichen, Wang Li, Li Guozhu

Prog Chem ›› 2024, Vol. 36 ›› Issue (4) : 471-485.

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Prog Chem ›› 2024, Vol. 36 ›› Issue (4) : 471-485. DOI: 10.7536/PC230911
Review

Machine Learning Assisted Molecule Design of Fuel

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Abstract

theoretical design of fuel has always been the focus of research about fuel in the area of propulsion technology.It can effectively overcome the complexity and potential danger of the experiment,and guide experimental synthesis of fuel,which can be verified by experimental results.It is anticipated that a new generation of fuel can be efficiently designed for subsequent fuel synthesis and application.However,the traditional Theoretical calculation methods,such as group contribution method and quantum chemical method,have the defects of low accuracy and efficiency.machine learning,a rapidly developed algorithm,has opened up a new way to design potential high-energy fuels,which exhibits strong capabilities in both property prediction and molecule design.in this review,several fuel molecule descriptors for machine learning are introduced,and different machine learning models for fuel property prediction and molecule design are briefed.Furthermore,the research on machine learning assisted property prediction and new molecule design of fuel is summarized,respectively.Finally,the challenges and future development of machine learning applied in fuel design are discussed。

Contents

1 Introduction

2 Fuel molecule description method

2.1 Molecular fingerprinting based on SMILES

2.2 Coulomb matrix

2.3 Continuous operable molecular entry specification

2.4 Molecule graph

3 Machine learning model

3.1 Model for fuel property prediction

3.2 Model for fuel molecule generation

4 Fuel property prediction

4.1 Single fuel property prediction

4.2 Multiple fuel properties prediction

5 Design of new fuel molecules

5.1 High throughput screening of fuel molecules

5.2 Reverse design of new fuel molecules

6 Conclusion and outlook

Key words

fuel / machine learning / molecule description / property prediction / molecule design / high-throughput screening

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Zhang Xiangwen , Hou Fang , Liu Ruichen , et al . Machine Learning Assisted Molecule Design of Fuel[J]. Progress in Chemistry. 2024, 36(4): 471-485 https://doi.org/10.7536/PC230911

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National Natural Science Foundation of China(22178248)
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