Ab Initio Investigation of O–H Dissociation from the Al–OH₂ Complex Using Molecular Dynamics and Neural Network Fitting

Abstract

The dissociation dynamics of the O-H bond in Al-OH2 is investigated on an approximated ab initio potential energy surface (PES). By adopting a dynamic sampling method, we obtain a database of 92 834 configurations. The potential energy for each point is calculated using MP2/6-311G (3df, 2p) calculations; then, a 60-neuron feed-forward neural network is utilized to fit the data to construct an analytic PES. The root-mean-square error (rmse) for the training set is reported as 0.0036 eV, while the rmse for the independent testing set is 0.0034 eV. Such excellent fitting accuracy indeed confirms the reliability of the constructed PES. Subsequently, quasi-classical molecular dynamics (MD) trajectories are performed on the constructed PES at various levels of vibrational excitation in the range of 1.03 to 2.23 eV to investigate the probability of O-H bond dissociation. The results indicate a linear relationship between reaction probability and internal energy, from which we can determine the minimum activation internal energy required for the dissociation as 0.62 eV. Moreover, the O-H bond rupture is shown to be highly correlated with the formation of Al-O bond.

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

“…We do this in the spirit of work carried out by many others 16,35,36,44 who fitted neural networks to single molecule potential surfaces. Given simple physical considerations, the sampling of the potential surface can be limited to a window of relevant energies.…”

Less is more: Sampling chemical space with active learning

“…[12][13][14][15][16][17][18][19] Specifically, ML approaches for the prediction of interatomic potential energy surfaces (referred to as ML potentials) have exhibited chemical accuracy compared to QM models at roughly the computational cost of classical force fields. [20][21][22][23][24][25][26][27][28][29][30][31] ML potentials promise to bridge the speed vs. accuracy gap between force fields and QM methods. Many recent studies rely on a philosophy of parametrization to one chemical system at a time 22,25 , single component bulk systems 27,28 or many equilibrium structures, i.e.…”

“…[20][21][22][23][24][25][26][27][28][29][30][31] ML potentials promise to bridge the speed vs. accuracy gap between force fields and QM methods. Many recent studies rely on a philosophy of parametrization to one chemical system at a time 22,25 , single component bulk systems 27,28 or many equilibrium structures, i.e. QM7 and QM9 datasets 32,33 .…”

Atomic Permutationally Invariant Polynomials for Fitting Molecular Force Fields

“…see Refs. 43,44) these aforementioned models had finite interaction range, resulting in linear scaling cost, which opened the door to simulations of large systems with unprecedented accuracy.…”