Intermolecular Interaction Predictions for Large Molecular Systems
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Physical Chemistry".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 473
Special Issue Editor
Interests: electronic structure theory; reaction design; machine learning; reaction mechanism; computational chemistry
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Intermolecular interactions, including van der Waals forces, hydrogen bonding, and electrostatic attractions, play an essential role in determining the properties and behaviors of various supramolecules and molecular materials. As molecular systems grow in complexity and size, the task of accurately predicting these interactions becomes increasingly challenging. Quantum mechanical methods have been instrumental in modeling intermolecular interactions, but they are limited to small and medium-sized systems due to their steep computational scaling. To overcome the challenge, researchers are increasingly turning to advanced computational techniques, such as low-scaling electron correlation methods and machine learning techniques, to enhance the accuracy and efficiency of computational predictions. Understanding and predicting intermolecular interactions in large molecular systems is essential for the development of novel materials, drugs, and biological systems. By leveraging cutting-edge computational methods and experimental techniques, researchers are paving the way for breakthroughs in these areas, with potential impacts across a wide range of scientific and technological fields.
This Special Issue is designed to gather scientific papers on intermolecular interaction predictions in large molecular systems. Progress in advanced computational methods, machine learning-based techniques, and experimental methods and other subjects can be discussed.
Prof. Dr. Shuhua Li
Guest Editor
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Keywords
- intermolecular interactions
- low scaling quantum chemistry
- machine learning
- experimental techniques
- computational predictions
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