Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis

Area of Science:

• Materials Science
• Chemical Engineering
• Computational Chemistry

Background:

• Rational design of inorganic materials is crucial for diverse applications.
• Conventional synthesis relies on trial-and-error, which is inefficient.
• Data-driven techniques offer a more predictable and controllable approach.

Purpose of the Study:

• To present a viewpoint on using data-driven techniques for inorganic materials synthesis.
• To compare design of experiments (DoE) and machine learning for synthesis control.
• To discuss challenges and perspectives on implementing these methods.

Main Methods:

• Comparative analysis of statistical DoE and machine learning.
• Review of recent literature case studies.
• Discussion on experimental bias and implementation challenges.

Main Results:

• Design choice (DoE vs. machine learning) impacts control, information gained, and cost.
• Case studies demonstrate the efficacy of data-driven synthesis.
• Experimental bias can influence outcomes.

Conclusions:

• Data-driven techniques significantly enhance predictability and control in inorganic materials synthesis.
• Widespread implementation faces challenges that need addressing.
• Future work should focus on overcoming these hurdles for robust materials design.