Predicting the Reactivity of Acyclic Silylenes and Germylenes in Hydrogen Activation Using Machine Learning.

Michelle Kleinhaus, Henning Remm, Prasenjit Palui, Alexander Linke, Viktoria H Gessner

Abstract read

In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Michelle Kleinhaus *Chair of Inorganic Chemistry II, Faculty of Chemistry and Biochemistry, Ruhr-University Bochum, Bochum, Germany.

Henning Remm *Chair of Inorganic Chemistry II, Faculty of Chemistry and Biochemistry, Ruhr-University Bochum, Bochum, Germany.

Prasenjit Palui *Chair of Inorganic Chemistry II, Faculty of Chemistry and Biochemistry, Ruhr-University Bochum, Bochum, Germany.ORCID https://orcid.org/0000-0001-9755-8446

Alexander LinkeChair of Inorganic Chemistry II, Faculty of Chemistry and Biochemistry, Ruhr-University Bochum, Bochum, Germany.

Viktoria H GessnerChair of Inorganic Chemistry II, Faculty of Chemistry and Biochemistry, Ruhr-University Bochum, Bochum, Germany.ORCID https://orcid.org/0000-0001-6557-2366

Funding

Deutsche Forschungsgemeinschaft 390677874Deutsche Forschungsgemeinschaft 497260357

6 · The paper itself

Abstract

Heavier carbenes can mimic transition metals in activating small molecules and strong bonds. Incorporating this ability into catalytic cycles demands careful, experimentally challenging tuning of their molecular properties via substituent variation. This study employs computational chemistry and data science to investigate dihydrogen activation by heavier carbenes to aid future candidate selection. Analyzing around 600 acyclic silylenes and germylenes derived from 40 different substituents, we found that a simple linear model predicts the activation barrier and reaction energy of H

Indexed as

bond activationscarbenesmachine learningmain group chemistrysilicon

Identifiers

PMID42801633

DOI10.1002/advs.77947

PMCPMC13616320

What OpenQuestion holds

Textmetadata

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Registered trials

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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.