ArticleJACS Au2022
Deep Learning-Assisted Investigation of Electric Field-Dipole Effects on Catalytic Ammonia Synthesis.
Article in JACS Au, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 55 citations in OpenAlex.
- High-Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Diffusion Model-Guided Inverse Design of Bimetallic Catalysts for Ammonia Decomposition.Journal of the American Chemical Society · 2026Article
- A Universal Machine Learning Framework Driven by Artificial Intelligence for Ion Battery Cathode Material Design.JACS Au · 2025Article
- Multiscale Simulation Guided Electric Field-Enhanced Ammonia Catalytic Cracking.ACS catalysis · 2025Article
- Elucidating the Role of Electric Fields in Fe Oxidation via an Environmental Atom Probe.Angewandte Chemie (International ed. in English) · 2025Article
- Integrating Physical Principles with Machine Learning for Predicting Field-Enhanced Catalysis.JACS Au · 2025Article
- Effects of Electric Field on Chemical Looping Combustion: A DFT Study of CO Oxidation on CuO (111) Surface.ACS omega · 2024Article
- Review
- Dipole field in nitrogen-enriched carbon nitride with external forces to boost the artificial photosynthesis of hydrogen peroxide.Nature communications · 2023Article
- Ambient Carbon-Neutral Ammonia Generation via a Cyclic Microwave Plasma Process.ACS applied materials & interfaces · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
External electric fields can modify binding energies of reactive surface species and enhance catalytic performance of heterogeneously catalyzed reactions. In this work, we used density functional theory (DFT) calculations-assisted and accelerated by a deep learning algorithm-to investigate the extent to which ruthenium-catalyzed ammonia synthesis would benefit from application of such external electric fields. This strategy allows us to determine which electronic properties control a molecule's degree of interaction with external electric fields. Our results show that (1) field-dependent adsorption/reaction energies are closely correlated to the dipole moments of intermediates over the surface, (2) a positive field promotes ammonia synthesis by lowering the overall energetics and decreasing the activation barriers of the potential rate-limiting steps (e.g., NH
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Registered trials
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.