biostudies-literatureLiu JNational Natural Science Foundation of ChinaTsinghua-Toyota Joint Research FundTsinghua Precision Medicine Foundation and Cross-Strait Tsinghua Research Institute Funde13771https://www.ebi.ac.uk/biostudies/studies/S-EPMC12697888biostudies-literatureUnknown12(46)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 (≈7²⁷ possibiliAdvanced science (Weinheim, Baden-Wurttemberg, Germany)Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.PMC126978885247126252175274Liu JPeng BWen PXu WWei YfalseHighly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.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 (≈7²⁷ possibili2025-01-01T00:00:00Z2025 Dec2026-06-06T01:21:23.723Z2026-05-24T03:11:42.715ZS-EPMC126978884098560610.1002/advs.202513771