Machine Learning Accelerated Computational Design of Bio-Inspired Catalysts in the Nitrogen Reduction Reaction.

Leonardo Di Ciano, Zihan You, Haoran Chen, Qifan Zhong, Rong-Zhen Liao, Shaoqi Zhan

Abstract read

In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Leonardo Di CianoDepartment of Chemistry-Ångström Laboratory, Molecular Biomimetics, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0009-0009-0417-8467

Zihan YouDepartment of Chemistry-Ångström Laboratory, Molecular Biomimetics, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0009-0005-6061-5765

Haoran ChenSchool of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0009-0008-9147-3941

Qifan ZhongSchool of Metallurgy and Environment, Central South University, Changsha, China.ORCID https://orcid.org/0000-0003-4963-0516

Rong-Zhen LiaoSchool of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0000-0002-8989-6928

Shaoqi ZhanDepartment of Chemistry-Ångström Laboratory, Molecular Biomimetics, Uppsala University, Uppsala, Sweden.ORCID https://orcid.org/0000-0002-6383-1771

Funding

National Natural Science Foundation of China 22571107The Swedish Research Council for Sustainable Development N 2023-01559

6 · The paper itself

Abstract

The development of efficient catalysts for nitrogen conversion to ammonia is critical for a sustainable alternative to the energy-intensive Haber-Bosch process. Yet, rational catalyst design remains highly challenging, compounded by complex structure-function relationships within realistic conditions. Herein, we present an integrated computational framework combining quantum chemical calculations with 27 machine learning models to predict experimental catalytic metrics in metal-ligand complexes. The models are trained and validated on a large experimental database and demonstrate high predictive accuracy across multiple tasks. For classification, family 1 and family 2 catalysts achieved test accuracies up to 1. Regression models yield test R

Indexed as

computational workflowfeature engineeringmachine learningnitrogen reduction reaction

Identifiers

PMID42250209

DOI10.1002/adma.73603

PMCPMC13361151

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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.