Multiscale Materials Modeling in an Industrial Environment

Area of Science:

• Materials Science
• Computational Chemistry
• Chemical Engineering

Background:

• The chemical industry relies on efficient research and development (R&D) for new materials.
• Predictive and rapid modeling are essential for competitive R&D.
• Handling complex systems requires robust and automated computational workflows.

Purpose of the Study:

• To review the industrial materials modeling process.
• To highlight the importance of predictive and fast modeling.
• To discuss approaches for building and parameterizing soft matter systems.

Main Methods:

• Review of existing materials modeling techniques in industry.
• Focus on approaches for soft matter system construction and parameterization.
• Integration of computational modeling with experimental validation.

Main Results:

• Predictive and fast modeling is a prerequisite for R&D success in the chemical industry.
• Stable, highly automated workflows are necessary for complex systems.
• Improved efficiency in new material development, particularly in polymer formulation, is achievable.

Conclusions:

• Intelligent combination of existing modeling techniques enhances product development.
• Materials modeling, when integrated with experimental work, offers significant value.
• Alignment with initiatives like the Materials Genome Initiative is beneficial.