Experimental discovery of novel ammonia synthesis catalysts <i>via</i> active learning

Jayarathna, Rasika; Onsree, Thossaporn; Drummond, Samuel; Naglic, Jennifer; Lauterbach, Jochen

doi:10.1039/d3ta05939a

Cited by 1 publication

(1 citation statement)

References 92 publications

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“…The work by Jayarathna et al (2024) introduces an active learning framework aimed at discovering new catalysts for ammonia synthesis under milder conditions, leveraging extensive literature data on heterogeneous catalysis. They primarily utilize ensemble learning algorithms, particularly the ETR.…”

Section: Data-driven Exploitationmentioning

confidence: 99%

From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

Benavides-Hernández,

Dumeignil

2024

ACS Catal.

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This review paper delves into synergistic integration of artificial intelligence (AI) and machine learning (ML) with highthroughput experimentation (HTE) in the field of heterogeneous catalysis, presenting a broad spectrum of contemporary methodologies and innovations. We methodically segmented the text into three core areas: catalyst characterization, data-driven exploitation, and data-driven discovery. In the catalyst characterization part, we outline current and prospective techniques used for HTE and how AI-driven strategies can streamline or automate their analysis. The data-driven exploitation part is divided into themes, strategies, and techniques that offer flexibility for either modular application or creation of customized solutions. In the data-driven exploration part we present applications that enable exploration of areas outside the experimentally tested chemical space, incorporating a section on computational methods for identifying new prospects. The review concludes by addressing the current limitations within the field and suggesting possible avenues for future research.

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Section: Data-driven Exploitationmentioning

confidence: 99%