Using Machine Learning for Green Substitution of Industrial Chemicals: Integrating Functionality, Hazard, and Life...

Area of Science:

• Green Chemistry
• Computational Chemistry
• Toxicology

Background:

• Industrial chemicals pose risks due to widespread use and environmental release.
• Current methods for designing safer chemicals may lead to regrettable substitutions.
• Generative machine learning (ML) presents a promising avenue for innovation.

Purpose of the Study:

• To review ML-assisted molecular design methodologies for green chemical alternatives.
• To propose design strategies for chemicals with reduced environmental hazards.
• To highlight research needs in AI for chemical risk management and green substitution.

Main Methods:

• Review of generative ML techniques for molecular design.
• Analysis of strategies for balancing chemical functionality with environmental safety.
• Case examples illustrating ML applications in chemical substitution.

Main Results:

• ML-assisted design can identify green alternatives to hazardous industrial chemicals.
• Proposed strategies enable the development of chemicals with desired functions and low lifecycle hazards.
• Identified areas for further research in AI-driven chemical design and risk assessment.

Conclusions:

• ML-assisted molecular design is crucial for developing sustainable industrial chemicals.
• AI agents can enhance chemical risk management and facilitate green substitution.
• Responsible chemical design using ML minimizes adverse human and environmental impacts.