Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data

Abstract

Ionic solvation phenomena in liquids involve intense interactions in the inner solvation shell. For interactions beyond the first shell, the ion–solvent interaction energies result from the sum of many smaller-magnitude contributions that can still include polarization effects. Deep neural network (DNN) methods have recently found wide application in developing efficient molecular models that maintain near-quantum accuracy. Here we extend the DeePMD-kit code to produce accurate molecular multipole moments in the bulk and near interfaces. The new method is validated by comparing the DNN moments with those generated by ab initio simulations. The moments are used to compute the electrostatic potential at the center of a molecular-sized hydrophobic cavity in water. The results show that the fields produced by the DNN models are in quantitative agreement with the AIMD-derived values. These efficient methods will open the door to more accurate solvation models for large solutes such as proteins.

Deep potentials for materials science

“…In these two examples, the high efficiency and accuracy of DP provided rapid screening of different properties to feedback into DFT exchange-correlation functional optimisation. Shi et al [182] extended DP to produce accurate molecular multipole moments in the bulk and near interfaces consistent with AIMD simulations. These moments were used to compute the electrostatic potential at the centre of a molecular-sized hydrophobic cavity in water.…”

A quantum chemical molecular dynamics repository of solvated ions

“…On the other hand, theoretical simulation techniques such as molecular dynamics (MD) can directly probe ion solvation structure (e.g., via radial distribution function, diffusion rates, coordination numbers, etc. ), delivering detailed insight into ion solvation structure in some cases [20][21][22][23][24][25][26][27][28][29][30][31][32] . The principal limitation with classical MD however is the variability in simulation parameters, such as the MD force field, ensemble, time integration algorithm etc.…”

Deep Potentials for Materials Science

“…See [103] for details. [176] (Hf0.2Zr0.2Ta0.2Nb0.2Ti0.2)X (X=C or B2) [177,178] Aqueous Systems water [96,[179][180][181][182][183][184][185][186][187][188][189][190]] zinc ion in water [191] water-vapor interface [192,193] water-TiO2 interface [194] ice [195,196] Molecular Systems and Clusters organic molecules [101,[197][198][199][200][201][202]] metal and alloy clusters [126,203] Surfaces and Low-dimensional Systems metal and alloy surfaces [105,126,136] graphane [132,204] monolayer In2Se3…”

“…In these two examples, the high efficiency and accuracy of DP provided rapid screening of different properties to feedback into DFT exchange-correlation functional optimisation. Shi, et al [189] extended DP to produce accurate molecular multipole moments in the bulk and near interfaces consistent with AIMD simulations. These moments were used to compute the electrostatic potential at the centre of a molecularsized hydrophobic cavity in water.…”