About this Research Topic
Functional molecules or materials, such as solvent, catalyst, adsorbent and working fluid, are widely used in process industry. Considering their significant effects, systematic methods for the optimal selection and design of these chemicals are crucial. Traditionally, the Process Systems Engineering (PSE) methods have been developed and used for the optimal synthesis, design, and control of macroscale manufacturing processes. However, as the impact of functional material selection on the process performance has gained increasing attention, the application domain of PSE has been largely broadened to support the decision-makings at the nanoscale related to the molecular or material structure. On the other hand, as experimental and computational data are becoming more readily available, materials informatics and data-driven methods have opened a door for the discovery of tailor-made functional materials for various applications.
The Research Topic seeks to advance the understanding of the current state and promote the research activities toward computer-aided molecular and material design using the conventional PSE methods or the modern machine learning and data-driven approaches.
We invite submissions of original research articles that focus on significant research contributions, as well as review articles summarizing the state-of-the-art and emphasizing opportunities and challenges. Manuscripts may focus on (but not be limited to) the following topics:
• Computer-aided chemical product design
• Computer-aided solvent design for reactions and separations
• Catalytic materials for energy and environment
• Porous materials (e.g., zeolite and metal-organic framework) for gas separation
• Heat transfer fluids (e.g., refrigerant and working fluid) design
• Energetic materials (e.g., photovoltaic material) design
• Big data analytics for materials science
• Machine learning and data-driven modeling for material design
• Modeling and optimization methods for computer-aided molecular/material design
• Mixed-integer programming for integrated molecular and process design
• Hybrid modeling for integrated material and process design.
Keywords: process systems engineering, computer-aided molecular design, functional material design, solvent design, catalyst design, adsorbent design, machine learning, hybrid modeling, big data, data-driven modeling, mathematical optimization, DFT calculation, molecular simulation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.