Evidence map›Paper›PMID 42842670›Full record

ArticleScience advances2026

AIMNet2-rxn: A machine-learned potential for generalized reaction modeling on a millions-of-pathways scale.

Dylan M Anstine, Qiyuan Zhao, Roman Zubatyuk, Shuhao Zhang, Veerupaksh Singla, Filipp Nikitin, Brett M Savoie, Olexandr Isayev

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 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

8 authors.

Dylan M AnstineDepartment of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213, USA.ORCID 0000-0002-4458-7080
Qiyuan ZhaoDepartment of Chemical Engineering, Purdue University, West Lafayette, IN 47907, USA.ORCID 0000-0003-3228-8160
Roman ZubatyukDepartment of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213, USA.ORCID 0000-0002-7285-9389
Shuhao ZhangDepartment of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213, USA.ORCID 0000-0002-5174-1875
Veerupaksh SinglaDepartment of Chemical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
Filipp NikitinDepartment of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213, USA.ORCID 0000-0002-1157-9471
Brett M SavoieDepartment of Chemical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.ORCID 0000-0002-7039-4039
Olexandr IsayevDepartment of Chemistry, Carnegie Mellon University, Pittsburgh, PA 15213, USA.ORCID 0000-0001-7581-8497

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mechanistic modeling of chemical transformations offers a compelling basis for understanding reactivity and allows for prediction of reaction outcomes before attempting experiments. Despite progress in machine-learned interatomic potentials (MLIPs), we demonstrate that available models lack the accuracy for diverse reaction modeling. With this motivation, we developed a general MLIP for mechanistic modeling of closed-shell carbon, hydrogen, nitrogen, and oxygen reactions, AIMNet2-rxn, using a dataset of ∼4.7 × 10

Identifiers

PMID42842670
PMCPMC13644750

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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.