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Machine learning enabled rational design of atomic catalysts for electrochemical reactions

MATERIALS CHEMISTRY FRONTIERS (2023)

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Summary: This study combines machine learning techniques and first-principle calculations to rapidly discover novel graphene-supported single-atom catalysts for nitrogen reduction reaction. By optimizing feature sets and constructing new catalytic descriptors, accurate predictions of high-performance single-atom catalysts can be made. This research is of great significance for the development of catalysts for nitrogen reduction reaction and rapid screening of new electrocatalysts.

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Article Chemistry, Physical

A study of two-dimensional single atom-supported MXenes as hydrogen evolution reaction catalysts using density functional theory and machine learning

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Review Chemistry, Multidisciplinary

Atomically Dispersed Heteronuclear Dual-Atom Catalysts: A New Rising Star in Atomic Catalysis

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SMALL (2022)

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Muhammad Umer et al.

Summary: This study highlights a new perspective for the rational design of transition metal single-atom catalysts (SACs) using density functional theory (DFT) and machine learning (ML) approaches. By embedding single atoms of 3d/4d/5d transition metals in different substrates, the electronic properties of 364 catalysts were tuned. Machine learning analysis identified stable and high-performance catalysts for hydrogen evolution reaction (HER) based on various descriptors. The study provides a fundamental understanding for the efficient rational design of TM-SACs for H-2 production through water-splitting.

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Review Chemistry, Physical

Single-atom catalysts for photocatalytic energy conversion

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Data-Driven High-Throughput Rational Design of Double-Atom Catalysts for Oxygen Evolution and Reduction

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ADVANCED FUNCTIONAL MATERIALS (2022)

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A Synthesis Strategy of Double-Atom Catalysts on a Carbon Surface

Lianping Wu et al.

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Flexible modulations on selectivity of syngas formation via CO2 reduction on atomic catalysts

Mingzi Sun et al.

Summary: This study systematically investigates syngas formation on graphene-based single atomic catalysts, finding that these catalysts can flexibly control the ratio of CO and H2 in syngas. Machine learning results indicate that accurate prediction of selectivity requires a comprehensive understanding of thermodynamic preference and electronic structures.

NANO ENERGY (2022)

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Machine Learning: A New Paradigm in Computational Electrocatalysis

Xu Zhang et al.

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Data-driven design of dual-metal-site catalysts for the electrochemical carbon dioxide reduction reaction

Haisong Feng et al.

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Review Chemistry, Physical

Machine learning for design principles for single atom catalysts towards electrochemical reactions

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Review Chemistry, Multidisciplinary

More is Different: Synergistic Effect and Structural Engineering in Double-Atom Catalysts

Yiran Ying et al.

Summary: Double-atom catalysts (DACs) have emerged as a novel frontier in heterogeneous catalysis, showing improved catalytic activity through synergistic effects while maintaining high atomic utilization efficiency, good selectivity, and stability. Current research focuses on the design, experimental and theoretical progress, and practical applications of DACs in various catalytic reactions.

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Article Chemistry, Multidisciplinary

Identifying the Activity Origin of a Cobalt Single-Atom Catalyst for Hydrogen Evolution Using Supervised Learning

Xinghui Liu et al.

Summary: This study reveals that the activity of graphene-based Co single-atom catalysts towards hydrogen evolution reaction is attributed to their edge sites, and edge-rich Co single atoms were synthesized based on theoretical results. By applying supervised learning techniques, the measured synchrotron spectrum of Co single atoms was interpreted, showing consistent local environments with Athena fitting. Moreover, the edge-rich Co single-atom catalysts exhibit better electrocatalytic performance than commercial Pt/C at high current density, indicating the potential for rational design and structural analysis of nanocatalysts.

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Single Metal Atom Catalyst Supported on g-C3N4 for Formic Acid Dehydrogenation: A Combining Density Functional Theory and Machine Learning Study

Xiaomei Zhao et al.

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JOURNAL OF PHYSICAL CHEMISTRY C (2021)

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Self-Validated Machine Learning Study of Graphdiyne-Based Dual Atomic Catalyst

Mingzi Sun et al.

Summary: Although the current progress of atomic catalysts is mainly limited to single atomic catalysts (SAC), with few successful cases of dual atomic catalysts (DAC), addressing challenges in thermodynamic stability and optimal metal combinations is crucial. Theoretical investigations on GDY-based DAC reveal that introducing selected lanthanide metals improves stability and electroactivity, with f-d orbital coupling identified as a key factor in modulating the d-band center for enhanced stability. These findings offer new insights into DAC and pave the way for advancements in atomic catalyst research.

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Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence

Zhong-Kang Han et al.

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NATURE COMMUNICATIONS (2021)

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Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts

Lianping Wu et al.

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ISCIENCE (2021)

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Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders

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Review Chemistry, Multidisciplinary

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Machine-Learning-Accelerated Catalytic Activity Predictions of Transition Metal Phthalocyanine Dual-Metal-Site Catalysts for CO2 Reduction

Xuhao Wan et al.

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