The article discusses the future of multiscale modeling in physical chemistry, emphasizing its growing importance in addressing complex scientific challenges. Multiscale modeling combines different methods to simulate systems at varying levels of approximation, from quantum mechanics to classical molecular dynamics, enabling the study of phenomena across a wide range of length and time scales. The authors highlight the need for this approach due to the limitations of current computational resources in simulating large systems from first principles. They argue that multiscale modeling will become a dominant research methodology in the coming years, driven by advances in theory, algorithms, and computing power. The article reviews existing multiscale approaches, including QM/Continuum, QM/MM, and QM/QM, and discusses their strengths and limitations. It also explores the potential of emerging techniques such as machine learning and quantum computing to enhance multiscale modeling. The authors propose a novel approach involving mobile quantum mechanics centers that can adapt to the system under study, enabling more accurate simulations of complex processes like natural photosynthesis. They conclude that multiscale modeling will play a crucial role in addressing global challenges such as climate change and energy supply, and that its widespread adoption in academia and industry is likely due to the development of user-friendly software and increased computational capabilities. The article underscores the importance of multiscale modeling in advancing our understanding of physical chemistry and its applications in various scientific fields.The article discusses the future of multiscale modeling in physical chemistry, emphasizing its growing importance in addressing complex scientific challenges. Multiscale modeling combines different methods to simulate systems at varying levels of approximation, from quantum mechanics to classical molecular dynamics, enabling the study of phenomena across a wide range of length and time scales. The authors highlight the need for this approach due to the limitations of current computational resources in simulating large systems from first principles. They argue that multiscale modeling will become a dominant research methodology in the coming years, driven by advances in theory, algorithms, and computing power. The article reviews existing multiscale approaches, including QM/Continuum, QM/MM, and QM/QM, and discusses their strengths and limitations. It also explores the potential of emerging techniques such as machine learning and quantum computing to enhance multiscale modeling. The authors propose a novel approach involving mobile quantum mechanics centers that can adapt to the system under study, enabling more accurate simulations of complex processes like natural photosynthesis. They conclude that multiscale modeling will play a crucial role in addressing global challenges such as climate change and energy supply, and that its widespread adoption in academia and industry is likely due to the development of user-friendly software and increased computational capabilities. The article underscores the importance of multiscale modeling in advancing our understanding of physical chemistry and its applications in various scientific fields.