{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T19:27:21Z","timestamp":1786476441428,"version":"build-2736575974"},"reference-count":34,"publisher":"Wiley","issue":"46","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52175274"],"award-info":[{"award-number":["52175274"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52471262"],"award-info":[{"award-number":["52471262"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Science"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Machine learning (ML) has surpassed traditional intuition\u2010driven trial\u2010and\u2010error approaches in metamaterial design by employing efficient inverse pipelines based on structure\u2013property 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\u2010quality training data, and excessive computational demands. To address these problems, Monte Carlo Tree Search\u2010Active Learning (MCTS\u2010AL), an active learning framework integrating Monte Carlo Tree Search (MCTS), convolutional neural networks (CNNs), and finite element method (FEM) to efficiently explore high\u2010performance 3D mechanical metamaterials using only 100 initial samples within a vast design space (\u22487\n                    <jats:sup>27<\/jats:sup>\n                    possibilities), is proposed. Demonstrated on triply periodic minimal surface (TPMS) metamaterials for stiffness and strength optimization, MCTS\u2010AL achieves 30% higher stiffness than uniform designs, an enhancement of strength of more than 20% compared with benchmark active learning methods (e.g., Bayesian Optimization, BO), and fewer iterations until convergence. T\u2010distributed Stochastic Neighbor Embedding (T\u2010SNE) clustering confirms that the superior performance stems from a comprehensive understanding of the design space and diverse sampling, with optimized structures forming distinct and various clusters. This work establishes a scalable, data\u2010efficient strategy for high\u2010dimensional mechanical metamaterial design and is expected to be applied in other scenarios demanding optimal solution exploration.\n                  <\/jats:p>","DOI":"10.1002\/advs.202513771","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T12:53:25Z","timestamp":1758632005000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search"],"prefix":"10.1002","volume":"12","author":[{"given":"Jiamu","family":"Liu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering Tsinghua University Beijing 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Peng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering Tsinghua University Beijing 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyun","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering University of Illinois Urbana\u2010Champaign  Urbana IL 61801 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Data Science City University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5656-1988","authenticated-orcid":false,"given":"Peng","family":"Wen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering Tsinghua University Beijing 100084 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_9_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmatsci.2017.12.003"},{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cocis.2019.02.008"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6633\/ace069"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41563-025-02119-8"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41563-025-02247-1"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202410428"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/adfm.202420892"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2024.113350"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.1c11136"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apmt.2021.101268"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mattod.2023.07.018"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.11.423"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmatsci.2023.101132"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruct.2024.118482"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2023.112103"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/adfm.202404165"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202302530"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmecsci.2022.107920"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-019-0221-0"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.3c00708"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtphys.2024.101549"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1088\/0256-307X\/40\/12\/124402"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202413805"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40854-1"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2111505119"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00762-x"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-020-0341-6"},{"key":"e_1_2_9_28_1","first-page":"1","volume":"54","author":"Ren P.","year":"2021","journal-title":"ACM Comp. 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