A Design-to-Device Pipeline for Data-Driven Materials Discovery.

Area of Science:

• Materials Science and Engineering
• Data Science and Artificial Intelligence
• Chemical Industry Innovation

Background:

• Traditional materials discovery relies on slow, unpredictable trial-and-error methods, creating a bottleneck for technological advancement.
• The "molecule-to-market" lead time for new materials averages 20 years, hindering industrial needs and innovation.
• Key sectors like environment, information storage, telecommunications, and catalysis require novel functional materials.

Purpose of the Study:

• To introduce and demonstrate a data-driven approach for accelerating materials discovery.
• To outline a systematic pipeline for designing and predicting new chemicals for specific applications.
• To showcase the potential of artificial intelligence and big data in revolutionizing materials innovation.

Main Methods:

• A four-step "design-to-device" pipeline: data extraction, data enrichment, material prediction, and experimental validation.
• Utilizing chemistry-aware natural-language processing (NLP) tools (e.g., ChemDataExtractor) to build massive chemical databases.
• Employing machine learning (ML) and high-performance computing (HPC) for data enrichment, pattern recognition, and prediction of new materials.

Main Results:

• Successful development of a data-driven strategy for accelerated materials discovery.
• Demonstrated utility of the pipeline for applications in photovoltaics, optics, and catalysis.
• Short-listing of lead candidate materials through data-mining workflows for experimental validation.

Conclusions:

• Data science and AI offer a transformative solution to the materials discovery bottleneck.
• The proposed "design-to-device" pipeline provides a roadmap for rapid identification of functional materials.
• This data-driven methodology is transferable across scientific disciplines, including biology and medicine.