Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction DOI
Nana Zhou, Yaling Zhao,

Qingzhang Lv

et al.

The Journal of Physical Chemistry C, Journal Year: 2024, Volume and Issue: 128(41), P. 17274 - 17281

Published: Oct. 8, 2024

The heteroatom-doped metallic compounds supported on conductive substrates are excellent catalysts for the hydrogen evolution reaction (HER) thanks to their tunable properties, e.g., and nonmetallic compositions, especially bimetallic active centers synergistic effect, as well morphology interaction between substrate. Only optimal combination these adjustable properties other external factors could endow remarkable HER catalytic activity of catalysts. Therefore, in this study, machine learning (ML) database based plenty from publicly available data was conducted train three different ML models, various features including electrolyte type, catalyst morphology, compositions (metallic nonmetallic) ratios, additive, substrate were analyzed figure out impacts overpotential (OP) values determine outstanding According feature importance Spearman coefficient analysis, metal elements ratio determined be Pt, Mo 0.5, heteroatoms nitrogen, sulfur, nickel foam. Finally, model predicts that foam nickel-supported composed Pt Mo2S3 codoped with nitrogen sulfur (N, S-doped Pt@Mo2S3) exhibits admirable performance alkaline electrolytes a pretty low OP value 33 mV. database-guided provides an alternative rapid screening prediction electrocatalysts.

Language: Английский

Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis DOI Creative Commons
Hoje Chun,

Jaclyn R. Lunger,

Jeung Ku Kang

et al.

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: Oct. 19, 2024

Single-atom catalysts (SACs) with multiple active sites exhibit high activity for a wide range of sluggish reactions, but identifying optimal multimetallic SAC is challenging due to the vast design space. Here, we present self-driving computational strategy that combines first-principles calculations and equivariant graph neural network (GNN) explore over 30,000 binary metallic varying combinations 3d transition metals different ligand environments oxygen reduction evolution reactions (ORR/OER). Active learning facilitates investigation search space by balancing exploration unseen atomic structures exploitation ones. The GNN learns chemical capture composition-structure-property relationships ORR/OER selectivity. predictions promising Co-Fe, Co-Co, Co-Zn metal pairs are consistent state-of-the-art results experimental measurements reported in literature. This approach can be extended broader class multi-element entropic materials systems.

Language: Английский

Citations

1
Ab initio calculations of high-entropy clusters for oxygen reduction and evolution as well as CO2 reduction reactions DOI
Mohsen Tamtaji, Mohammad Kazemeini, Jafar Abdi

et al.

Applied Surface Science, Journal Year: 2024, Volume and Issue: unknown, P. 161555 - 161555

Published: Oct. 1, 2024

Language: Английский

Citations

1
Machine learning assisted screening of nitrogen-doped graphene-based dual-atom hydrogen evolution electrocatalysts DOI

Huijie Zhang,

Qiang Wei, Shuaichong Wei

et al.

Molecular Catalysis, Journal Year: 2024, Volume and Issue: 570, P. 114649 - 114649

Published: Nov. 7, 2024

Language: Английский

Citations

1
Application of machine learning for material prediction and design in the environmental remediation DOI
Yunzhe Zheng,

Si Sun,

Jiali Liu

et al.

Chinese Chemical Letters, Journal Year: 2024, Volume and Issue: unknown, P. 110722 - 110722

Published: Dec. 1, 2024

Language: Английский

Citations

1
Using Machine Learning to Forecast the Conductive Substrate-Supported Heteroatom-Doped Metal Compound Electrocatalysts for Hydrogen Evolution Reaction DOI
Nana Zhou, Yaling Zhao,

Qingzhang Lv

et al.

The Journal of Physical Chemistry C, Journal Year: 2024, Volume and Issue: 128(41), P. 17274 - 17281

Published: Oct. 8, 2024

The heteroatom-doped metallic compounds supported on conductive substrates are excellent catalysts for the hydrogen evolution reaction (HER) thanks to their tunable properties, e.g., and nonmetallic compositions, especially bimetallic active centers synergistic effect, as well morphology interaction between substrate. Only optimal combination these adjustable properties other external factors could endow remarkable HER catalytic activity of catalysts. Therefore, in this study, machine learning (ML) database based plenty from publicly available data was conducted train three different ML models, various features including electrolyte type, catalyst morphology, compositions (metallic nonmetallic) ratios, additive, substrate were analyzed figure out impacts overpotential (OP) values determine outstanding According feature importance Spearman coefficient analysis, metal elements ratio determined be Pt, Mo 0.5, heteroatoms nitrogen, sulfur, nickel foam. Finally, model predicts that foam nickel-supported composed Pt Mo2S3 codoped with nitrogen sulfur (N, S-doped Pt@Mo2S3) exhibits admirable performance alkaline electrolytes a pretty low OP value 33 mV. database-guided provides an alternative rapid screening prediction electrocatalysts.

Language: Английский

Citations

0