Cyanoborohydride (CBH)-based hypergolic coordination compounds for versatile fuels DOI

Linna Liang,

Ye Zhong, Yiqiang Xu

et al.

Chemical Engineering Journal, Journal Year: 2021, Volume and Issue: 426, P. 131866 - 131866

Published: Aug. 18, 2021

Language: Английский

Machine learning accelerates the materials discovery DOI

Jiheng Fang,

Ming Xie,

Xingqun He

et al.

Materials Today Communications, Journal Year: 2022, Volume and Issue: 33, P. 104900 - 104900

Published: Nov. 9, 2022

Language: Английский

Citations

84
Machine learning for design principles for single atom catalysts towards electrochemical reactions DOI
Mohsen Tamtaji, Hanyu Gao, Md Delowar Hossain

et al.

Journal of Materials Chemistry A, Journal Year: 2022, Volume and Issue: 10(29), P. 15309 - 15331

Published: Jan. 1, 2022

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom (SACs) through establishment deep structure–activity relationships.

Language: Английский

Citations

71
Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives DOI Creative Commons

Xiaolan Tian,

Siwei Song, Fang Chen

et al.

Energetic Materials Frontiers, Journal Year: 2022, Volume and Issue: 3(3), P. 177 - 186

Published: Aug. 18, 2022

Predicting chemical properties is one of the most important applications machine learning. In recent years, prediction energetic materials using learning has been receiving more attention. This review summarized advances in predicting compounds' (e.g., density, detonation velocity, enthalpy formation, sensitivity, heat explosion, and decomposition temperature) Moreover, it presented general steps for applying to practical from aspects data, molecular representation, algorithms, accuracy. Additionally, raised some controversies specific its possible development directions. Machine expected become a new power driving soon.

Language: Английский

Citations

51
Descriptors applicability in machine learning-assisted prediction of thermal decomposition temperatures for energetic materials: Insights from model evaluation and outlier analysis DOI
Zhixiang Zhang, Chao Chen,

Yilin Cao

et al.

Thermochimica Acta, Journal Year: 2024, Volume and Issue: 735, P. 179717 - 179717

Published: March 6, 2024

Language: Английский

Citations

9
Analysis and evaluation of machine learning applications in materials design and discovery DOI
Mahsa Golmohammadi, Masoud Aryanpour

Materials Today Communications, Journal Year: 2023, Volume and Issue: 35, P. 105494 - 105494

Published: Jan. 25, 2023

Language: Английский

Citations

20
A review of quantum chemical methods for treating energetic molecules DOI Creative Commons
Shitai Guo, Jian Liu, Wen Qian

et al.

Energetic Materials Frontiers, Journal Year: 2021, Volume and Issue: 2(4), P. 292 - 305

Published: Nov. 30, 2021

As a necessary tool for understanding, prediction, and design (especially on microscopic scale), Quantum chemical (QC) methods have profound impact the field of energetic materials (EMs). This study focuses upon QC applicable to molecules their related applications. They generally include Hartree-Fock method, semi-empirical methods, density functional theory (DFT), high-accuracy ab initio methods. includes detailed discussion about application scope accuracy descriptions geometric structure, electronic thermodynamic property, reactivity molecules. Additionally, this stresses machine learning combined with DFT calculations that becomes increasingly popular as an important way establish models accurate property predictions. work is expected be instructive constructive use in EM study.

Language: Английский

Citations

34
High-throughput design of energetic molecules DOI
Jian Liu,

Shicao Zhao,

Bowen Duan

et al.

Journal of Materials Chemistry A, Journal Year: 2023, Volume and Issue: 11(45), P. 25031 - 25044

Published: Jan. 1, 2023

High-throughput design of energetic molecules implemented by molecular docking, AI-aided design, an automated computation workflow, a structure−property database, deep learning QSPRs and easy-to-use platform.

Language: Английский

Citations

15
Predicting the enthalpy of formation of energetic molecules via conventional machine learning and GNN DOI
Di Zhang, Qingzhao Chu, Dongping Chen

et al.

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(8), P. 7029 - 7041

Published: Jan. 1, 2024

Different ML models are used to map the enthalpy of formation from molecular structure, and impact different feature representation methods on results is explored. Among them, GNN achieve impressive results.

Language: Английский

Citations

6
Advancements in methodologies and techniques for the synthesis of energetic materials: A review DOI Creative Commons
Wei Du, Lei Yang, Jing Feng

et al.

Energetic Materials Frontiers, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

Recent years have witnessed significant advancements in methodologies and techniques for the synthesis of energetic materials, which are expected to shape future manufacturing applications. Techniques including continuous flow chemistry, electrochemical synthesis, microwave-assisted biosynthesis been extensively employed pharmaceutical fine chemical industries and, gratifyingly, found broader This review comprehensively introduces recent utilization these emerging techniques, aiming provide a catalyst development novel green methods synthesizing materials.

Language: Английский

Citations

6
Machine learning-assisted single-cell Raman fingerprinting for in situ and nondestructive classification of prokaryotes DOI
Nanako Kanno, Shingo Kato, Moriya Ohkuma

et al.

iScience, Journal Year: 2021, Volume and Issue: 24(9), P. 102975 - 102975

Published: Aug. 11, 2021

Language: Английский

Citations

29