Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.

Jiamu Liu, Bo Peng, Weiyun Xu, Ye Wei, Peng Wen

Abstract read

In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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.

Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
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

5 authors.

Jiamu LiuState Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.

Bo PengState Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.

Weiyun XuDepartment of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.

Ye WeiDepartment of Data Science, City University of Hong Kong, Hong Kong, China.

Peng WenState Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.ORCID https://orcid.org/0000-0002-5656-1988

Funding

National Natural Science Foundation of China 52175274National Natural Science Foundation of China 52471262Tsinghua Precision Medicine Foundation and Cross-Strait Tsinghua Research Institute FundTsinghua-Toyota Joint Research Fund

6 · The paper itself

Abstract

Machine learning (ML) has surpassed traditional intuition-driven trial-and-error approaches in metamaterial design by employing efficient inverse pipelines based on structure-property mapping. However, three critical challenges impede the applications of ML when extending the geometry from 2D to 3D: exponentially increasing design space dimensionality, scarce high-quality training data, and excessive computational demands. To address these problems, Monte Carlo Tree Search-Active Learning (MCTS-AL), an active learning framework integrating Monte Carlo Tree Search (MCTS), convolutional neural networks (CNNs), and finite element method (FEM) to efficiently explore high-performance 3D mechanical metamaterials using only 100 initial samples within a vast design space (≈7

Indexed as

active learningmachine learningmechanical metamaterialsMonte Carlo tree search

Identifiers

PMID40985606

DOI10.1002/advs.202513771

PMCPMC12697888

What Socratic holds

Textmetadata

LicenceCC BY

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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 Socratic graph.