Can we predict materials that can be synthesised?

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• Materials discovery is critical for technological advancement but is often slow and resource-intensive.
• Computational methods accelerate materials screening but often predict materials that are difficult to synthesize.
• Researchers face challenges in validating the synthetic realizability of novel materials.

Purpose of the Study:

• To address the challenge of synthesizing computationally predicted materials.
• To review methods for confirming the synthetic feasibility of hypothetical materials across the entire discovery pipeline.
• To reduce wasted effort in experimental materials discovery programs.

Main Methods:

• Consideration of all stages of materials discovery: component sourcing, synthesis, and processing.
• Integration of computational predictions with experimental validation strategies.
• Discussion of potential 'recipes' for laboratory preparation accompanying material predictions.

Main Results:

• Identification of key bottlenecks in translating computational predictions to laboratory synthesis.
• Highlighting the need for a holistic approach considering synthesis and processing.
• Demonstrating the potential to accelerate novel materials discovery through improved feasibility assessment.

Conclusions:

• Confirming synthetic realizability is crucial for efficient materials discovery.
• Integrating synthesis planning into computational prediction can prevent wasted experimental effort.
• This approach can significantly accelerate the development of new technologies through faster materials innovation.