Evidence map›Paper›PMID 40133294›Full record

ArticleNature communications2025

Auto-resolving the atomic structure at van der Waals interfaces using a generative model.

Wenqiang Huang, Yucheng Jin, Zhemin Li, Lin Yao, Yun Chen, Zheng Luo, Shen Zhou, Jinguo Lin, Feng Liu, Zhifeng Gao and 5 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Generative Models for Crystalline Materials.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  3. Review
  4. Article
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

15 authors.

Wenqiang Huang *School of Advanced Materials, Peking University Shenzhen Graduate School, Shenzhen, China.ORCID http://orcid.org/0009-0007-1667-3559
Yucheng Jin *State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.
Zhemin Li *College of Science, National University of Defense Technology, Changsha, China.
Lin YaoDP Technology, Beijing, China. yaol@dp.tech.ORCID http://orcid.org/0000-0002-3437-5607
Yun ChenDepartment of Materials Science and Engineering, College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.
Zheng LuoDepartment of Materials Science and Engineering, College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China.
Shen ZhouCollege of Science, National University of Defense Technology, Changsha, China.ORCID http://orcid.org/0000-0001-5682-2589
Jinguo LinState Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing, China.
Feng LiuState Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing, China.ORCID http://orcid.org/0000-0001-9987-782X
Zhifeng GaoDP Technology, Beijing, China.ORCID http://orcid.org/0000-0001-8433-999X
Jun ChengState Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.ORCID http://orcid.org/0000-0001-6971-0797
Linfeng ZhangDP Technology, Beijing, China.
Fangping OuyangSchool of Physics, Central South University, Changsha, China. oyfp@csu.edu.cn.ORCID http://orcid.org/0000-0001-7904-3430
Jin ZhangSchool of Advanced Materials, Peking University Shenzhen Graduate School, Shenzhen, China. jinzhang@pku.edu.cn.ORCID http://orcid.org/0000-0003-3731-8859
Shanshan WangSchool of Advanced Materials, Peking University Shenzhen Graduate School, Shenzhen, China. wangshanshan08@nudt.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52021006National Natural Science Foundation of China (National Science Foundation of China) 52222201, 52172032Shenzhen Science and Technology Innovation Commission KQTD20221101115627004
6 · The paper itself

Abstract

The high-resolution visualization of atomic structures is significant for understanding the relationship between the microscopic configurations and macroscopic properties of materials. However, a rapid, accurate, and robust approach to automatically resolve complex patterns in atomic-resolution microscopy remains difficult to implement. Here, we present a Trident strategy-enhanced disentangled representation learning method (a generative model), which utilizes a few unlabelled experimental images with abundant low-cost simulated images to generate a large corpus of annotated simulation data that closely resembles experimental results, producing a high-quality large-volume training dataset. A structural inference model is then trained via a residual neural network which can directly deduce the interlayer slip and rotation of diversified and complicated stacking patterns at van der Waals (vdW) interfaces with picometer-scale accuracy across various materials (e.g. MoS

Identifiers

PMID40133294
PMCPMC11937378

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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.