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From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding
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Machine Learning-Aided Crystal Facet Rational Design with Ionic Liquid Controllable Synthesis.

Fuming Lai1,2, Zhehao Sun3,4, Sandra Elizabeth Saji3

  • 1Materials Interfaces Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 440305, China.

Small (Weinheim an Der Bergstrasse, Germany)
|March 3, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning (ML)-aided framework for designing crystal facets and controllable synthesis using ionic liquids. The approach enables precise customization of crystal facets and facet junctions for advanced materials.

Keywords:
controllable synthesiscrystal facetsionic liquidsmachine learningrational design

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Area of Science:

  • Materials Science
  • Crystallography
  • Chemical Engineering

Background:

  • Crystallographic facets offer unique properties and functionalities but are challenging to predict, customize, and synthesize.
  • Controlling crystal facets and their junctions is crucial for developing advanced functional materials.

Purpose of the Study:

  • To develop a machine learning (ML)-aided framework for rational crystal facet design.
  • To integrate ML-driven design with ionic liquid controllable synthesis for on-demand customization of crystal facets and junctions.
  • To demonstrate the framework using anatase titanium dioxide (TiO2).

Main Methods:

  • Utilized machine learning (ML) to predict surface energies from facet junction data.
  • Established relationships between surface energy and growth conditions via Langmuir adsorption isotherm.
  • Employed ionic liquids, specifically [bmim][BF4], for controllable synthesis of TiO2 crystals with tailored facets.

Main Results:

  • Successfully predicted surface energies and unveiled relationships between surface energy and growth conditions.
  • Developed controllable synthetic strategies for custom crystal facets and facet junctions.
  • Verified the framework by synthesizing TiO2 crystals with desired facets and junctions using ionic liquid tuning.

Conclusions:

  • The developed framework effectively integrates data-intensive rational design with experimental synthesis.
  • Demonstrated the feasibility of ML-aided design for customizing crystallographic facets and facet junctions.
  • Highlights the potential of intelligent chemistry for accelerating the discovery of facet-governed functional materials.