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cond-mat.mtrl-sci2026
Cluster-based Structural Similarity for Dataset Visualization and Data Selection for Machine Learning Interatomic Potentials
Yuto Iwasaki, Miguel A. Caro
Machine learning interatomic potentials (MLIPs) are essential components for accelerating simulation-driven materials design. Data-efficient MLIP training relies on data-selection…
cond-mat.mtrl-sci2025
Active Learning of a Neural Network Potential for Large-Scale Atomistic Simulations of Polymer Electrolyte Membranes
Yuta Yoshimoto, Naoki Matsumura, Meguru Yamazaki +3
Machine learning interatomic potentials (MLIPs) can achieve near density-functional-theory (DFT) accuracy at force-field computational cost; however, long-time, large-scale molecul…