Two-tier machine learning acceleration of molecular dynamics with enhanced sampling: surface reactions and restructuring on metal catalysts

Efficient molecular dynamics(MD) are critical for energy landscape exploration and reaction free energy computation. For heterogeneous catalysis, it is prohibitive to directly compute these reactions with ab initio molecular dynamics. To solve this problem, we introduce a two-tier machine learning approach to accelerate MD simulations. First, a single point calculation is accelerated by replacing DFT force calculations with the Tensor-Field Neural Network force field. Second, reaction coordinates learned are learned with multi-task neural networks and are employed to guide enhanced sampling to further accelerate the estimation of free energy barriers. This framework is applied to model formate dehydrogenation, a key reaction in fuel cells running with formic acid. Au(110) and Cu(110) surfaces are chosen as the model catalysts. The simulations sample free energy landscape and reveal how different initial formate coverages affect surface restructuring of the catalysts.