{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Liu J"],"funding":["National Natural Science Foundation of China","Tsinghua-Toyota Joint Research Fund","Tsinghua Precision Medicine Foundation and Cross-Strait Tsinghua Research Institute Fund"],"pagination":["e13771"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12697888"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(46)"],"pubmed_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<sup>27</sup> possibili"],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search."],"pmcid":["PMC12697888"],"funding_grant_id":["52471262","52175274"],"pubmed_authors":["Liu J","Peng B","Wen P","Xu W","Wei Y"],"additional_accession":[]},"is_claimable":false,"name":"Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.","description":"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<sup>27</sup> possibili","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-06T01:21:23.723Z","creation":"2026-05-24T03:11:42.715Z"},"accession":"S-EPMC12697888","cross_references":{"pubmed":["40985606"],"doi":["10.1002/advs.202513771"]}}