Structure prediction of crystals, surfaces and nanoparticles

Area of Science:

• Materials Science
• Computational Chemistry
• Physics

Background:

• Predicting the structure of crystals, surfaces, and nanoparticles is crucial for understanding material properties.
• Existing computational methods face challenges in efficiency and accuracy for complex systems.

Purpose of the Study:

• To review and summarize current computational techniques for structure prediction.
• To discuss the evolving role of machine learning in materials discovery.
• To provide examples across metallic, inorganic, and organic systems.

Main Methods:

• Summary of major search algorithms used in structure prediction.
• Overview of various energy functions employed in computational modeling.
• Discussion of machine learning-based approaches, including deep learning and reinforcement learning.

Main Results:

• Machine learning methods are demonstrating significant promise in accelerating structure prediction.
• A diverse range of systems, including metals, ceramics, and organic molecules, can be studied using these techniques.
• The integration of dynamic *in situ* microscopy offers new avenues for validating predicted structures.

Conclusions:

• Computational structure prediction is a rapidly advancing field.
• Machine learning is becoming an indispensable tool for exploring chemical space and designing novel materials.
• Further development of integrated computational and experimental approaches will drive future discoveries.