Chemical data intelligence for sustainable chemistry.

Area of Science:

• Chemical Engineering
• Data Science
• Sustainable Chemistry

Background:

• The chemical industry faces pressure to adopt sustainable practices and reduce environmental impact.
• Current methods for identifying sustainable reaction routes are manual, time-consuming, and rely on chemical intuition.
• Digitalization of chemical data presents new opportunities for process optimization.

Purpose of the Study:

• To review methods for automated discovery and assessment of sustainable reaction routes.
• To explore the potential of chemical data intelligence for a circular chemical economy.
• To identify bottlenecks and opportunities in data, evaluation metrics, and decision-making for sustainable chemistry.

Main Methods:

• Review of state-of-the-art methods in chemical data intelligence.
• Analysis of three key transition areas: data, evaluation metrics, and decision-making.
• Elucidation of synergies and interfaces between these areas.

Main Results:

• Automated discovery and assessment of reaction routes are feasible with digitized chemical data.
• Chemical data intelligence can identify inherently more sustainable pathways.
• Data-related challenges (completion, linkage) are current bottlenecks but offer significant advancement opportunities.

Conclusions:

• The digitalization of chemical data offers a transformative opportunity for selecting optimal and sustainable reaction routes.
• Addressing data bottlenecks is crucial for realizing the full potential of chemical data intelligence in sustainable chemistry.
• Integrated approaches across data, metrics, and decision-making are key to advancing a circular chemical economy.