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This paper introduces a budget-dependent method that combines coverage-based and response-based training-set selection strategies for machine-learned interatomic potentials, aiming to improve accuracy and efficiency in materials simulations.
This paper introduces BDIP-Net, a graph neural network for predicting properties of bilayer materials by explicitly modeling intra-layer and inter-layer interactions, and presents a framework for efficient structure optimization using MatterSim-D3.
PhononBench-MP40 is a benchmark dataset of phonon stability labels and spectra for over 46,000 Materials Project-derived crystals, designed to evaluate workflow-defined phonon stability and support materials screening.