J Am Chem Soc June 2024 PMID 38830239

Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed Ruthenium Dissolution.

Jehad Abed, Javier Heras-Domingo, Rohan Yuri Sanspeur, Mingchuan Luo, Wajdi Alnoush, Debora Motta Meira, Hsiaotsu Wang, Jian Wang, Jigang Zhou, Daojin Zhou, Khalid Fatih, John R Kitchin, Drew Higgins, Zachary W Ulissi, Edward H Sargent

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Peak: 20 in Jun 2024

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The overall tone indicates a strong interest in how AI accelerates catalyst discovery for energy applications, supported by examples like the mention of the university of Toronto’s use of AI and references to collaboration with groups such as Sargent's. The language emphasizes advancements and potential, with phrases like 'identifies stability' and 'significant breakthroughs,' reflecting a positive outlook on the research’s importance and future impact.

AgreementModerate agreement

Most discussions recognize the significance of the research, with some highlighting the innovative use of AI for catalyst discovery, indicating moderate to strong agreement on its importance.

InterestHigh interest

The posts reflect high interest, evidenced by mentions of collaboration, technological advancements, and references to the novelty of the machine learning framework.

EngagementModerate engagement

While the engagement is positive, it mostly revolves around sharing summaries and highlighting the publication's highlights without deep technical debates.

ImpactModerate impact

The discussions suggest that the publication could influence future research directions, especially in sustainable energy and catalyst stability, though the perceived immediate impact varies.

Abstract in brief

The rising demand for green hydrogen has sparked concern over the supply of iridium, crucial for catalysts in oxygen evolution reactions.
A machine learning-based computational pipeline has been developed to predict the stability of over 2,000 metallic oxides, which successfully identifies RuCrTiO as a highly durable candidate for use as a catalyst.
Experimental results show that RuCrTiO demonstrates a notable performance and stability, reducing overpotential and mass dissolution, while a detailed analysis reveals mechanisms behind its improved activity and stability compared to traditional catalysts.

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Machine Learning-Identified Stable Catalysts for Acidic Water Oxidation

Water splitting is essential for renewable hydrogen production. This work studies 36,000 mixed metal oxides using machine learning to identify stable catalysts, with RuCrTiO showing enhanced stability and activity over…

John Kitchin · Jun 8, 2024 279 views

Machine Learning Identifies Durable Acidic Water Oxidation Catalysts for Green Hydrogen

This podcast discusses a machine learning pipeline developed to identify highly stable catalysts for oxygen evolution reactions in acidic conditions, crucial for green hydrogen production. It highlights experimental…

John Kitchin · Feb 21, 2025 72 views

American Elements

@AmericanElement · X

University of Toronto researchers use AI to accelerate scientific breakthroughs in the search for sustainable energy https://t.co/yeajZgHT5Y

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Steve Balman

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AI— green hydrogen Source: ACS Publications https://t.co/xJfUvjRdhK

Sep 2, 2024 · 1,694 followers View post →

balam sinhue lopez

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RT @chayito09: Fuente: https://t.co/WJzOUPlz4b Artículo original https://t.co/7UipLacYWR

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ferminacevesd

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RT @chayito09: Fuente: https://t.co/WJzOUPlz4b Artículo original https://t.co/7UipLacYWR

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Rosario in Paris ᥫ᭡

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Fuente: https://t.co/WJzOUPlz4b Artículo original https://t.co/7UipLacYWR

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Carnegie Mellon Chemical Engineering

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RT @CanLightSource: Featured #paper: "Pourbaix Machine Learning Framework Identifies Acidic Water Oxidation Catalysts Exhibiting Suppressed…

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CanadianLightSource

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Carnegie Mellon Chemical Engineering

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