High throughput methodology for synthesis, screening, and optimization of solid state lithium ion electrolytes.

Area of Science:

• Solid-state chemistry
• Materials science
• Thin-film deposition

Background:

• Lithium ion conductors are crucial for advanced energy storage.
• Exploring the Li(3x)La(2/3-x)TiO(3) system is key to developing new battery materials.
• High-throughput methods accelerate the discovery of novel materials.

Purpose of the Study:

• To systematically investigate the Li(3x)La(2/3-x)TiO(3) solid solution and its composition space.
• To identify optimal compositions for high ionic conductivity.
• To develop an efficient synthesis and analysis methodology.

Main Methods:

• High-throughput physical vapor deposition for material synthesis.
• Artificial neural network modeling for conductivity prediction.
• Partition analysis, multivariate curve resolution, and principal component analysis for data interpretation.

Main Results:

• An optimal total ionic conductivity of 5.45 × 10(-4) S cm(-1) was achieved for Li(0.17)La(0.29)Ti(0.54) (x = 0.11).
• Key synthetic parameters and their thresholds were identified using partition analysis.
• Phase distribution diagrams were constructed, mapping phases to compositional zones.

Conclusions:

• The high-throughput synthesis approach offers advantages in compositional range and material quality.
• Informatics tools are essential for analyzing large datasets in materials discovery.
• This study demonstrates a powerful approach for optimizing ion conductor materials.