Deep Learning for Molecules and Materials

Area of Science:

• Chemistry and Materials Science
• Artificial Intelligence
• Computational Science

Background:

• Deep learning (DL) is increasingly vital in scientific research.
• Existing DL resources lack focus on chemistry and materials science applications.
• The unique challenges of molecular data require specialized DL approaches.

Purpose of the Study:

• To provide a systematic introduction to deep learning for chemistry and materials science.
• To bridge the gap in current educational materials for DL in these fields.
• To equip researchers with the knowledge to apply DL to molecular data.

Main Methods:

• Covers fundamental mathematics for DL.
• Explains essential machine learning concepts.
• Details common neural network architectures.
• Provides practical guidance for implementation.

Main Results:

• A comprehensive overview of DL principles and techniques.
• Specific examples of DL applications in chemistry and materials science.
• Foundational knowledge for practitioners in the field.

Conclusions:

• The textbook serves as a crucial resource for scientists entering DL.
• It addresses the specific needs of researchers working with molecular data.
• The 'living document' approach ensures content remains current.