Accelerated Deep Learning Dynamics for Atomic Layer Deposition of Al(Me)₃ and Water on OH/Si(111)

Knowledge of the detailed mechanism behind the atomic layer deposition (ALD) can greatly facilitate the optimization of the manufacturing process. Computational modeling can potentially foster the understanding; however, the presently available capabilities of the accurate ab initio computational techniques preclude their application to modeling surface processes occurring on a long time scale, such as ALD. Although the situation can be greatly improved using machine learning (ML), this technique requires an enormous amount of data for training datasets. Here, we propose an iterative protocol for optimizing ML training datasets and apply ML-assisted ab initio calculations to model surface reactions occurring during the Al(Me)₃/H₂O ALD process on the OH-terminated Si (111) surface. The protocol uses a recently developed low-dimensional projection technique (TDUS), greatly reducing the amount of information required to achieve high accuracy (ca. 1 kcal/mol or less) of the developed ML models. The resulting free energy landscapes reveal fine details of various aspects of the target ALD process, such as the surface proton transfer, zwitterionic surface configurations, elimination-addition/addition-elimination, and S_N2 reactions as well as the role of the surface entropic and temperature effects. Simulations of adsorption dynamics predict that the maximum physisorption rate of ca. 70% is achieved at the incidence velocity u_rms of the reactants in the range of 15-20 Å/ps. Hence, the proposed protocol furnishes a very effective tool to study complex chemical reaction dynamics at a much reduced computational cost.