ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022
Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Article
- Nonequilibrium Electrochemical Synthesis of Cu-In Intermetallic Compound Nanoparticles for Selective COThe journal of physical chemistry letters · 2026Article
- Reactivity of Gas-Phase Metal Clusters: Unlocking Mechanisms of Activation and Conversion of Small Molecules.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Data-driven discovery of Pt single atom embedded germanosilicate MFI zeolite catalysts for propane dehydrogenation.Nature communications · 2025Article
- Computational Design of Catalysts with Experimental Validation: Recent Successes, Effective Strategies, and Pitfalls.The journal of physical chemistry. C, Nanomaterials and interfaces · 2024Review
- Advancements and prospects of perovskite-based fuel electrodes in solid oxide cells for COChemical science · 2024Review
- Ten-electron count rule for the binding of adsorbates on single-atom alloy catalysts.Nature chemistry · 2024Article
- Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures.Advanced materials (Deerfield Beach, Fla.) · 2024Review
- Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction.Nano-micro letters · 2023Review
- Machine Learning Descriptors for Data-Driven Catalysis Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling.iScience · 2023Review
- Prediction of transition state structures of gas-phase chemical reactions via machine learning.Nature communications · 2023Article
- Recent advances in density functional theory approach for optoelectronics properties of graphene.Heliyon · 2023Review
- First-Principles Microkinetic Modeling Unravelling the Performance of Edge-Decorated Nanocarbons for Hydrogen Production from Methane.ACS applied materials & interfaces · 2023Article
- Theoretical insights into the methane catalytic decomposition on graphene nanoribbons edges.Frontiers in chemistry · 2023Article
- Prediction and Construction of Energetic Materials Based on Machine Learning Methods.Molecules (Basel, Switzerland) · 2022Review
- Identifying Descriptors for Promoted Rhodium-Based Catalysts for Higher Alcohol SynthesisACS catalysis · 2022Article
- Tungsten-Based Nanocatalysts: Research Progress and Future Prospects.Molecules (Basel, Switzerland) · 2022Review
- Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
At present, alloys have broad application prospects in heterogeneous catalysis, due to their various catalytic active sites produced by their vast element combinations and complex geometric structures. However, it is the diverse variables of alloys that lead to the difficulty in understanding the structure-property relationship for conventional experimental and theoretical methods. Fortunately, machine learning methods are helpful to address the issue. Machine learning can not only deal with a large number of data rapidly, but also help establish the physical picture of reactions in multidimensional heterogeneous catalysis. The key challenge in machine learning is the exploration of suitable general descriptors to accurately describe various types of alloy catalysts, which help reasonably design catalysts and efficiently screen candidates. In this review, several kinds of machine learning methods commonly used in the design of alloy catalysts is introduced, and the applications of various reactivity descriptors corresponding to different alloy systems is summarized. Importantly, this work clarifies the existing understanding of physical picture of heterogeneous catalysis, and emphasize the significance of rational selection of universal descriptors. Finally, the development of heterogeneous catalytic descriptors for machine learning are presented.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.