Enhancing Studies on Molecular Systems through Quantum Mechanical Refinements and Artificial Intelligence

The study of molecular systems resides within the intersecting fields of conventional experimental and theoretical approaches, traditionally anchored in classical mechanics. While these methods have yielded significant insights, their attempts to incorporate quantum mechanical effects often fall short, resulting in systematic deviations. This necessitates novel approaches to bridge existing gaps. Recent advancements emphasize the need to integrate quantum mechanical considerations more seamlessly into computer simulations and experimental studies of molecular systems. Moreover, the emergence of artificial intelligence and machine learning provides additional avenues for enhancing our understanding and treatment of these systems.

This Research Topic aims to explore innovative strategies to address the limitations of classical approaches by embedding quantum mechanical refinements in the study of molecular systems. The spotlight is on leveraging computational simulations enhanced with AI methodologies to develop a deeper and more accurate understanding of molecular behaviors and interactions. This includes refining interaction potentials, enhancing the treatment of statistical mechanics in molecular systems, and validating theoretical models through cutting-edge experimental studies.

To gather further insights in the domain of computational and statistical analysis of molecular systems, we welcome articles addressing, but not limited to, the following themes:
- The development and refinement of quantum-informed computer simulations
- Creation of novel interaction potentials integrating classical and quantum principles
- Exploration of quantum mechanical effects on statistical mechanics for molecular systems
- Groundbreaking experimental studies that challenge or corroborate simulations
- The use of AI and ML as enhancement tools in computer simulations
- Real-world applications and case studies of quantum-informed models

This Research Topic invites contributions from multiple disciplines to contribute original research articles, reviews, and perspectives. We envision this interdisciplinary collaborative effort will lead to significant technological advancements and new theoretical models in the field, pushing the boundaries of what is currently achievable in molecular system studies.

This Research Topic was inspired by the work of Prof. Fernando del Río Haza.