Researchers have developed improved machine-learning interatomic potentials trained on large, diverse DFT datasets. The new approach enables more accurate and efficient atomistic simulations across a wider range of materials, reducing the need for system-specific retraining and lowering reliance on costly first-principles calculations.
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