Introducing the Data Analytics Approach to Molecular Systems

By applying a data analytics approach to molecular systems, we can draw parallels between social groups and assemblies of molecules. This innovative approach introduces concepts of statistical mechanics, machine learning, and data science. It demonstrates how individual molecular features and interactions between molecules, or “communication” processes, can predict properties and collective behavior of molecular systems. This is similar to how polling and social networking can provide insights into the behavior of social groups.

Key Features of the Data Analytics Approach

This approach offers several key features that make it a valuable tool for researchers and scientists:

• Data Analytics in Molecular Systems: This approach applies data analytics techniques to molecular systems, providing a new perspective on understanding molecular interactions and behaviors.
• Artificial Intelligence and Machine Learning: By leveraging artificial intelligence and machine learning, researchers can analyze molecular trends and make predictions about molecular systems.
• Applications in Biological, Environmental, and Energy Research: This approach has applications in cutting-edge research areas such as biology, environmental science, and energy. It can help scientists understand complex molecular systems and their behaviors in these fields.
• Molecular Simulation and Computational Sciences: The data analytics approach is linked with other important techniques and trends in computational sciences. This includes molecular simulation, which can provide detailed insights into molecular interactions and behaviors.

Authors’ Expertise and Reputation

The authors of this book have a well-established track record and reputation in the field of molecular systems and data analytics. Their expertise ensures that the information presented is accurate, up-to-date, and relevant to current research trends.

Publication Details

Publication Date: January 29, 2024

Language: English

ISBN: 9780367438937, 9781003832430