Deep potential-driven structure exploration of ice polymorphs.

Yuefeng Lei, Xiangyang Liu, Yaochen Yu, Haiyang Niu

Abstract read

In one paragraph

Article in Innovation (Cambridge (Mass.)), 2025. 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

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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

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

4 authors.

Yuefeng LeiState Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.

Xiangyang LiuState Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.

Yaochen YuState Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.

Haiyang NiuState Key Laboratory of Solidification Processing, School of Materials Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ice, a ubiquitous substance in nature, exhibits diverse forms under varying temperature and pressure conditions. However, our understanding of ice polymorphs remains incomplete. The directional nature of hydrogen bonding and the complexity of the networks they form pose significant challenges to computational studies of ice structures. In this work, we present an extensive exploration of ice polymorphs under pressure conditions ranging from 1 bar to 10 GPa. We employ an advanced crystal-structure-prediction scheme that integrates an evolutionary algorithm, an active-learning deep neural network potential, and molecular dynamics simulations with

Indexed as

crystal structure predictiondeep neural networkice polymorphsnew ice phase

Identifiers

PMID40432774

DOI10.1016/j.xinn.2025.100881

PMCPMC12105487

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LicenceCC BY-NC-ND

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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.