One-dimensional vs. two-dimensional proton transport processes at solid–liquid zinc-oxide–water interfaces

Abstract

Neural network molecular dynamics simulations unravel the long-range proton transport properties of ZnO–water interfaces.

Free energy of proton transfer at the water–TiO2 interface from ab initio deep potential molecular dynamics

“…Several authors have demonstrated the DNNs' ability to reproduce the complex PES of ab initio potentials, [32][33][34] thus allowing accurate long-time simulations of several condensed phase systems [35][36][37] including metal oxide-water interfaces. 38,39 In the present study, the ab initio-based DNN potential reproduces energy and atomic forces obtained from DFT at a ve orders of magnitude lower computational cost. This allows us to carry out MD simulations at the nanosecond timescale on systems of thousands of atoms and to use enhanced sampling techniques to obtain converged free energy surfaces of proton transfer at the aqueous anatase (101) interface as a function of suitable reaction coordinates.…”

Proton-transfer dynamics in ionized water chains using real-time time-dependent density functional theory

“…In the condensed phase, water molecules organize themselves into large cluster structures, strongly connected by hydrogen-bond networks [25,26]. Frequently (aqueous) solvated semiconductor surfaces sustain and enhance the formation of such networks [11,[27][28][29][30], resulting in the ordered formation of surface chains of water molecules. Thus the very well-developed network of H bonds in water plays a fundamental role in not only defining its structural and dynamical properties but also the properties of the material it interacts with [31,32].…”

Machine learning for the modeling of interfaces in energy storage and conversion materials

“…Copyright (2017) American Chemical Society.) differs from that over ZnO (1010) [98] and also investigated in further detail the structure of the water near ZnO surfaces [99].…”