Mechanism-Guided Descriptor for Hydrogen Evolution Reaction in 2D Ordered Double Transition-Metal Carbide MXenes DOI Creative Commons
Junmei Du,

Yifan Yan,

Xiu‐Mei Li

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Selecting effective catalysts for the hydrogen evolution reaction (HER) among MXenes remains a complex challenge. While machine learning (ML) paired with density functional theory (DFT) can streamline this search, issues training data quality, model accuracy, and descriptor selection limit its effectiveness. These hurdles often arise from an incomplete understanding of catalytic mechanisms. Here, we introduce mechanism-guided (δ) HER, designed to enhance catalyst screening ordered transition metal carbide MXenes. This integrates structural energetic characteristics, derived in-depth analysis orbital interactions relationship between Gibbs free energy adsorption (ΔG H) features. The proposed H = -0.49δ - 2.18) not only clarifies structure-activity links but also supports efficient, resource-effective identification promising catalysts. Our approach offers new framework developing descriptors advancing screening.

Language: Английский

Active phase discovery in heterogeneous catalysis via topology-guided sampling and machine learning DOI Creative Commons

Shisheng Zheng,

Ximing Zhang,

Heng-Su Liu

et al.

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 14, 2025

Understanding active phases across interfaces, interphases, and even within the bulk under varying external conditions environmental species is critical for advancing heterogeneous catalysis. Describing these through computational models faces challenges in generation calculation of a vast array atomic configurations. Here, we present framework automatic efficient exploration phases. This approach utilizes topology-based algorithm leveraging persistent homology to systematically sample configurations diverse coordination environments material morphologies. Simultaneously, machine learning force fields enable rapid computations. We demonstrate effectiveness this two systems: hydrogen absorption Pd, where penetrates subsurface layers bulk, inducing "hex" reconstruction CO2 electroreduction, explored 50,000 sampled configurations; oxidation dynamics Pt clusters, oxygen incorporation renders clusters less during reduction reactions, investigated 100,000 In both cases, predicted their impacts on catalytic mechanisms closely align with previous experimental observations, indicating that proposed strategy can model complex systems discovery specific conditions. Discovering heterocatalysis entails configuration sampling optimization. authors developed based topology effectively explore structures, applied electroreduction Oxygen Reduction Reaction

Language: Английский

Citations

1
Electrochemical CO2 Reduction on SnO: Insights into C1 Product Dynamic Distribution and Reaction Mechanisms DOI Creative Commons
Zhongyuan Guo, Tianyi Wang,

Heng Liu

et al.

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 3173 - 3183

Published: Feb. 6, 2025

The precise synthesis of desirable products from the electrochemical CO2 reduction reaction (CO2RR) remains challenging, primarily due to unclear structure–activity relationships under in situ conditions. Recognized by their cost-effectiveness and nontoxic nature, Sn-based materials are extensively utilized CO2RR produce valuable chemicals. Notably, our large-scale data mining experimental literature reveals a significant trend: SnO2-based electrocatalysts generate HCOOH, while SnO-based counterparts demonstrate ability both HCOOH CO comparable quantities. Furthermore, findings indicate that SnO underexplored terms its surface speciation for compared materials. Addressing these issues is crucial field electrocatalysis, as understanding them will not only clarify why uniquely influences distribution C1 but also provide insights into how precisely control electrocatalytic processes targeted product synthesis. Herein, we employed constant-potential method combined with coverage reconstruction analyses simulate energetics intermediates elucidate dynamic on resting typical Our analysis effectively identifies active involved CO2RR. comparative simulations between pristine reconstructed surfaces reveal electrochemistry-induced oxygen vacancies direct distribution. By addressing critical issues, aim advance electrocatalysis contribute chemical production CO2, stimulating future exploration conditions other systems.

Language: Английский

Citations

0
Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives☆ DOI Creative Commons
Xue Jia, Tianyi Wang, Di Zhang

et al.

Journal of Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 116162 - 116162

Published: April 1, 2025

Language: Английский

Citations

0
Mechanism-Guided Descriptor for Hydrogen Evolution Reaction in 2D Ordered Double Transition-Metal Carbide MXenes DOI Creative Commons
Junmei Du,

Yifan Yan,

Xiu‐Mei Li

et al.

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Selecting effective catalysts for the hydrogen evolution reaction (HER) among MXenes remains a complex challenge. While machine learning (ML) paired with density functional theory (DFT) can streamline this search, issues training data quality, model accuracy, and descriptor selection limit its effectiveness. These hurdles often arise from an incomplete understanding of catalytic mechanisms. Here, we introduce mechanism-guided (δ) HER, designed to enhance catalyst screening ordered transition metal carbide MXenes. This integrates structural energetic characteristics, derived in-depth analysis orbital interactions relationship between Gibbs free energy adsorption (ΔG H) features. The proposed H = -0.49δ - 2.18) not only clarifies structure-activity links but also supports efficient, resource-effective identification promising catalysts. Our approach offers new framework developing descriptors advancing screening.

Language: Английский

Citations

0