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

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 (≈7²⁷ possibili

SUBMITTER: Liu J 

PROVIDER: S-EPMC12697888 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature