Abstract

Vanadium dioxide (VO2), a highly important thermotropic phase-transition material, undergoes a reversible metal-insulator transition near temperatures of 68 °C and has potential applications in the energy-efficient smart window field. Recently, it has been found that the phase-transition temperature of VO2 films with nanometer thickness approached the ideal of room temperature (30 °C); however, deciphering this nanoscale phase-transition mechanism is still difficult in experiments and computer simulations. Here, a full-scale computational model was carefully constructed using molecular dynamics and first principles based on the artificial neural net algorithm and fully validated via accurate atomic layer deposition experiments. Overall, the machine-learning training results indicated that experimental observations and numerical simulation were in good agreement, demonstrating the feasibility and accuracy of our full-scale nanothermodynamics model. The computational simulations showed that the surface 3-layer atomic occupancy of the VO2 nanomaterials was a critical regulator of the phase transition temperature, implying as an important strategy for phase transition behavior in large-scale VO2 nanofilms designed on first principles. Moreover, the precise atomic layer deposition results agree with the simulation calculations by 98%, revealing that the computational model effectively and accurately combines with the experiments, and thus can accurately regulate the phase transition temperature of the VO2 nanofilm. This work was of crucial importance for potential applications of VO2 film in smart windows.

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