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Enhanced sampling methods such as metadynamics and umbrella sampling havebecome essential tools for exploring the configuration space of molecules andmaterials. At the same time, they have long faced a number of issues such asthe inefficiency when dealing with a large number of collective variables (CVs)or systems with high free energy barriers. In this work, we show that with\redc{the clustering and adaptive tuning techniques}, the reinforced dynamics(RiD) scheme can be used to efficiently explore the configuration space andfree energy landscapes with a large number of CVs or systems with high freeenergy barriers. We illustrate this by studying various representative andchallenging examples. Firstly we demonstrate the efficiency of adaptive RiDcompared with other methods, and construct the 9-dimensional free energylandscape of peptoid trimer which has energy barriers of more than 8 kcal/mol.We then study the folding of the protein chignolin using 18 CVs. In this case,both the folding and unfolding rates are observed to be equal to 4.30~$\mus^{-1}$. Finally, we propose a protein structure refinement protocol based onRiD. This protocol allows us to efficiently employ more than 100 CVs forexploring the landscape of protein structures and it gives rise to an overallimprovement of 14.6 units over the initial Global Distance Test-High Accuracy(GDT-HA) score.

Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics