Reverse design of high-detonation-velocity organic energetic compounds based on an accurate BPNN with wide applicability DOI
Qiong Wu,

Guan-chen Dong,

Shuai-yu Wang

и другие.

Journal of Materials Chemistry A, Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

Key factors affecting detonation velocity ( D ) are identified with machine learning (2% error), and high- energetic compounds designed.

Язык: Английский

Harnessing Zinc Stannate for Sustainable Energy and Environment Solutions: Advances in Photocatalytic, Piezocatalytic, and Piezo-photocatalytic Technologies DOI
Kaiqi Wang,

Ziying Guan,

Yiming He

и другие.

Nano Energy, Год журнала: 2024, Номер unknown, С. 110518 - 110518

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

16

Rational Design of Carbon‐Based Electrocatalysts for H2O2 Production by Machine Learning and Structural Engineering DOI
Rong Ma, Gao‐Feng Han, Li Feng

и другие.

Advanced Energy Materials, Год журнала: 2025, Номер unknown

Опубликована: Апрель 17, 2025

Abstract Electrochemical synthesis of hydrogen peroxide (H 2 O ) via two‐electron oxygen reduction reaction (2e − ORR) represents an economically viable alternative to conventional anthraquinone processes. While noble metal catalysts have dominated this field, carbon‐based materials are emerging as promising alternatives due their low cost, abundant reserves, and tunable properties. This mini‐review summarizes recent advances in computational methods, particularly the integration density functional theory (DFT) with machine learning (ML), accelerate rational design electrocatalysts by enabling rapid screening structure‐training predictions. Meanwhile, optimization strategies systematically investigated, focusing on four key aspects: atomic‐level heterochromatic doping, defect engineering, microenvironment control, morphological design. Despite significant progress achieving high selectivity activity, challenges remain scaling these for industrial applications. Moving H will require multidisciplinary efforts combining advanced situ characterization techniques, modeling, process engineering develop robust suitable diverse operating conditions.

Язык: Английский

Процитировано

0

Task Decomposition Strategy Based on Machine Learning for Boosting Performance and Identifying Mechanisms in Heterogeneous Activation of Peracetic Acid Process DOI
Wei Zhuang,

Xiao Zhao,

Qianqian Luo

и другие.

Water Research, Год журнала: 2024, Номер 267, С. 122521 - 122521

Опубликована: Сен. 26, 2024

Язык: Английский

Процитировано

2

Reverse design of high-detonation-velocity organic energetic compounds based on an accurate BPNN with wide applicability DOI
Qiong Wu,

Guan-chen Dong,

Shuai-yu Wang

и другие.

Journal of Materials Chemistry A, Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

Key factors affecting detonation velocity ( D ) are identified with machine learning (2% error), and high- energetic compounds designed.

Язык: Английский

Процитировано

1