Evidence map›Paper›PMID 40369028›Full record

ArticleScientific reports2025

Representing Born effective charges with equivariant graph convolutional neural networks.

Alex Kutana, Koji Shimizu, Satoshi Watanabe, Ryoji Asahi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Alex KutanaNagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan.
Koji ShimizuNational Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.
Satoshi WatanabeThe University of Tokyo, Hongo, Tokyo, Japan.
Ryoji AsahiNagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan. ryoji.asahi@chem.material.nagoya-u.ac.jp.

Funding

Core Research for Evolutional Science and Technology JPMJCR1996Japan Society for the Promotion of Science 23H04105
6 · The paper itself

Abstract

Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the independence of the property on the choice of the reference frame. Here we explicitly encode such properties using an equivariant graph convolutional neural network. The network respects rotational symmetries of the crystal throughout by using equivariant weights and descriptors and provides a tensorial output of the target value. Applications to tensors of atomic Born effective charges in diverse materials including perovskite oxides, Li

Indexed as

Equivariant graph convolutional neural networksLinear responseOxidesPhysics-informed neural networksTensor of atomic Born effective charges

Identifiers

PMID40369028
PMCPMC12078725

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.