A Vision for the Future of Multiscale Modeling.

Area of Science:

• Physical Chemistry
• Computational Science

Background:

• Modern computer science has driven progress in physical chemistry.
• Current computational resources are insufficient for first-principles simulation of extended systems.
• Biological, catalytic, and photophysical processes necessitate accurate, yet computationally expensive, treatments.

Purpose of the Study:

• To review multiscale modeling approaches in physical chemistry.
• To highlight achievements, drawbacks, and future directions.
• To explore the application of advanced methods to scientific challenges like light-harvesting processes.

Main Methods:

• Coupling theories and algorithms at different scales (quantum mechanics, molecular dynamics, continuum electrodynamics).
• Dividing systems into domains with varying levels of approximation.
• Developing simultaneous multiscale calculations for dynamic systems.

Main Results:

• Multiscale modeling has yielded remarkable results over 60 years.
• Emerging computational techniques like machine learning and quantum computing offer new possibilities.
• Advanced methods can address complex scientific challenges, such as simulating natural photosynthesis.

Conclusions:

• Multiscale modeling is poised for significant growth as a dominant research methodology.
• Future developments will integrate new computational techniques for enhanced accuracy and scope.
• This approach promises enormous progress in physical chemistry and societal benefits.