Amorphous TiO2 (a-TiO2) is widely used in many fields, ranging from photoelectrochemistry to bioengineering, hence detailed knowledge of its atomic structure is of scientific and technological interest. Here we use an ab initio-based deep neural network potential (DP) to simulate large scale atomic models of crystalline and disordered TiO2 with molecular dynamics. Our DP reproduces the structural properties of all 11 TiO2 crystalline phases, predicts the densities and structure factors of molten and amorphous TiO2 with only a few percent deviation from experiments, and describes the pressure dependence of the amorphous structure in agreement with recent observations. It can be extended to model additional structures and compositions, and can be thus of great value in the study of TiO2-based nanomaterials.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Physics and Astronomy (miscellaneous)