Accurate yet transferable machine-learning interatomic potentials are essential for accelerating materials and chemical discovery. However, many existing universal models are overfitted to narrow ...

illustrating the comprehensive zero-shot benchmark of 19 universal machine learning interatomic potentials and the dominant impact of training data composition for surface energy prediction. A ...

For decades, quantum-mechanical simulations, with density functional theory (DFT) at the forefront, have defined the benchmark for predicting materials’ properties. However, the emergence of ...