Discovery of High-Efficient Dual-atom Catalysts for Propane Dehydrogenation Assisted by Machine Learning DOI
Xianpeng Wang,

Yanxia Ma,

Youyong Li

et al.

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(33), P. 22286 - 22291

Published: Jan. 1, 2024

Propane dehydrogenation (PDH) is a highly efficient approach for industrial production of propylene, and the dual-atom catalysts (DACs) provide new pathways in advancing atomic catalysis PDH with dual active sites. In this work, we have developed an strategy to identify promising DACs reaction by combining high-throughput density functional theory (DFT) calculations machine-learning (ML) technique. By choosing γ-Al

Language: Английский

Modulating Selectivity and Stability of the Direct Seawater Electrolysis for Sustainable Green Hydrogen Production DOI Creative Commons
Dazhi Yao, Chun Liu, Yanzhao Zhang

et al.

Materials Today Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 100089 - 100089

Published: Feb. 1, 2025

Language: Английский

Citations

1
Recent Advances in Single- and Dual-Atom Catalysts for Efficient Nitrogen Electro-Reduction and Their Perspectives DOI Creative Commons
Joyjit Kundu, Toshali Bhoyar,

Saehyun Park

et al.

Advanced Powder Materials, Journal Year: 2025, Volume and Issue: unknown, P. 100279 - 100279

Published: Feb. 1, 2025

Language: Английский

Citations

0
Applications of machine learning in surfaces and interfaces DOI Open Access
Shaofeng Xu, Jing‐Yuan Wu, Ying Guo

et al.

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Language: Английский

Citations

0
P-block cathode electrocatalysts: A critical review of their role and impact on oxygen reduction reaction in fuel cells applications DOI

Siti Haziyah Mohd Chachuli,

Sharifah Najiha Timmiati, Kee Shyuan Loh

et al.

Journal of Industrial and Engineering Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Machine Learning‐Driven Selection of Two‐Dimensional Carbon‐Based Supports for Dual‐Atom Catalysts in CO2 Electroreduction DOI Creative Commons
Zhen Tan, Xinyu Li, Yanzhang Zhao

et al.

ChemCatChem, Journal Year: 2024, Volume and Issue: 16(22)

Published: Aug. 12, 2024

Abstract The electrocatalytic reduction of carbon dioxide by metal catalysts featuring dual‐atomic active sites, supported on two‐dimensional carbon‐nitrogen materials, holds promise for enhanced efficiency. potential synergy between various support materials and transition compositions in influencing reaction performance has been recognized. However, systematic studies the selection optimal remain limited, primarily due to intricate structure dual‐atom generating a variety adsorption sites. Incorporating influence further amplifies computational challenges, doubling already substantial calculation requirements. This study addresses this challenge introducing machine learning approach expedite identification most stable intermediate sites simultaneous prediction energy. innovative method significantly reduces costs, enabling consideration materials. We explore use both graphene‐like (g−)C 2 N g‐C 9 4 revealing their main distinction capacity *CHO. variation is attributed different C : ratios site through distinct charge transfer conditions. Our findings offer valuable insights design optimization catalysts.

Language: Английский

Citations

1
Discovery of High-Efficient Dual-atom Catalysts for Propane Dehydrogenation Assisted by Machine Learning DOI
Xianpeng Wang,

Yanxia Ma,

Youyong Li

et al.

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(33), P. 22286 - 22291

Published: Jan. 1, 2024

Propane dehydrogenation (PDH) is a highly efficient approach for industrial production of propylene, and the dual-atom catalysts (DACs) provide new pathways in advancing atomic catalysis PDH with dual active sites. In this work, we have developed an strategy to identify promising DACs reaction by combining high-throughput density functional theory (DFT) calculations machine-learning (ML) technique. By choosing γ-Al

Language: Английский

Citations

0