Machine Learning-Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis

Crystallographic facets in a crystal carry interior properties and proffer rich functionalities in a wide range of application areas. However, rational prediction, on-demand customization, and accurate synthesis of facets and facet junctions of a crystal are enormously desirable but still challenging. Herein, a framework of machine learning (ML)-aided crystal facet design with ionic liquid controllable synthesis is developed and then demonstrated with the star-material anatase TiO2. Aided by employing ML to acquire surface energies from facet junction datasource, the relationships between surface energy and growth conditions based on the Langmuir adsorption isotherm are unveiled, enabling to develop controllable facet synthetic strategies. These strategies are successfully verified after applied for synthesizing TiO2 crystals with custom crystal facets and facet junctions under tuning ionic liquid [bmim][BF4] experimental conditions. Therefore, this innovative framework integrates data-intensive rational design and experimental controllable synthesis to develop and customize crystallographic facets and facet junctions. This proves the feasibility of an intelligent chemistry future to accelerate the discovery of facet-governed functional material candidates.

Automated summary

This study introduces a framework for machine learning aided crystal facet design and controllable ionic liquid synthesis, demonstrated using TiO2 crystals. By utilizing machine learning to acquire surface energies from facet junction data sources, the relationships between surface energy and growth conditions are revealed, leading to the development of controllable facet synthetic strategies.