High-Throughput Methods in Materials Science (Part I): A Review of Chemical and Physical Methods and Automated Sample...

Area of Science:

• Materials Science
• Chemical Engineering
• Robotics

Background:

• Advancement in materials science using artificial intelligence (AI) and machine learning (ML) is limited by insufficient experimental data, termed data starvation.
• Automated synthesis of solid materials, especially polymers and composites, presents significant engineering challenges compared to solution-based chemistry.
• Traditional research models are prone to human bias, including the non-publication of negative results, which hinders AI model development by omitting crucial design space information.

Purpose of the Study:

• To define the architecture of a fully automated, unbiased
• data factory
• for closed-loop materials discovery.
• To focus on the physical foundations of a high-throughput (HT) workflow, encompassing experimental planning, automated synthesis, and material management.
• To present a paradigm shift from discrete Design of Experiments (DoE) to Continuous Gradient DoE for materials development.

Main Methods:

• Review of robotic platforms with precise gravimetric/volumetric feeders, extruders, and in-line capillary rheology for seamless HT manufacturing of thermoplastics and composites.
• Presentation of an innovative Continuous Material Management approach for sample logistics, including physical tagging (inkjet marking), spool-based transport, and real-time metadata mapping.
• Emphasis on the integration of automated synthesis and management systems to create a reliable hardware and analytical infrastructure.

Main Results:

• Demonstrated an order-of-magnitude increase in productivity, generating tens of thousands of novel material variants annually.
• Achieved a radical reduction in unit costs for material synthesis and characterization.
• Produced terabytes of standardized, machine-readable data crucial for AI-driven materials engineering.

Conclusions:

• Establishing a reliable, automated hardware and analytical infrastructure is essential for unlocking the full potential of AI in advanced materials engineering.
• The developed HT workflow and Continuous Material Management system address data starvation and human bias in materials discovery.
• This foundational work paves the way for efficient, AI-accelerated discovery of novel materials.