  Training machine learning interatomic potentials often requires optimizing a
loss function composed of three variables: potential energies, forces, and
stress. The contribution of each variable to the total loss is typically
weighted using fixed coefficients. Identifying these coefficients usually
relies on iterative or heuristic methods, which may yield sub-optimal
  results. To address this issue, we propose an adaptive loss weighting
algorithm that automatically adjusts the loss weights of these variables during
the training of potentials, dynamically adapting to the characteristics of the
training dataset. The comparative analysis of models trained with fixed and
adaptive loss weights demonstrates that the adaptive method not only achieves a
more balanced predictions across the three variables but also improves overall
prediction accuracy.
