To increase the efficiency of the AL loop and avoid redepositing identical Gaussians at approximately the same positions of the PES at the beginning of each AL iteration, we introduce the WTMetaD-IB approach, employing an inherited bias scheme (Fig. ). In this method, the bias potential generated by WTMetaD from the previous iteration (highlighted by pink and labelled as I -1 in Fig. ) is carried forward to the current iteration. During the current iteration (I), the new WTMetaD Gaussians are thus deposited atop the existing inherited potential, ensuring a varying bias in each AL iteration (right panel, Fig. ). When multiple MLP-MD simulations with WTMetaD-enhanced bias are executed in parallel, the bias potential generated for each process, highlighted by light orange in Fig. , is averaged and used in the next AL iteration, resulting in a smoother bias potential. This smoothing effect is beneficial as it further mitigates the artificial roughness of the potential caused by the frequent deposition of steep Gaussians. Additionally, the starting point of MLP-MD in each iteration is updated based on the lowest biased energy (DFT energy + inherited bias energy) among the points in the current dataset to prioritize unexplored regions (right panel, Fig. ). By introducing the inherited bias scheme using the updated starting points, our sampling method demonstrates increased efficiency relative to WTMetaD sampling. A comparative analysis of WTMetaD and WTMetaD-IB is presented below.