Crystal Structure Prediction via Efficient Sampling of the Potential Energy Surface

Area of Science:

• Materials Science
• Computational Chemistry
• Physics

Background:

• Crystal structure prediction (CSP) is crucial for discovering new materials across physics, chemistry, and materials science.
• Predicting crystal structures computationally presents challenges due to vast configuration spaces and potential energy surfaces (PES).

Purpose of the Study:

• To summarize advances in CSP methods and their impact on exploring the PES.
• To present the CALYPSO method as a case study for prominent CSP issues and strategies.
• To highlight the role of CSP in guiding experimental synthesis of novel materials.

Main Methods:

• Review of major CSP method developments and PES exploration fundamentals.
• Detailed overview of the CALYPSO methodology, including its theory, features, and strategies for balancing exploration and exploitation.
• Case studies demonstrating theory-driven material discovery using CSP.

Main Results:

• Significant progress in CSP algorithms enables computational research to predict novel material forms and properties.
• The CALYPSO method offers effective strategies for efficient PES exploration.
• Successful application of CSP in discovering high-temperature superconducting superhydrides and other atypical compounds.

Conclusions:

• CSP is revolutionizing materials discovery by guiding experimental synthesis.
• Further research in CSP holds promise for addressing outstanding challenges and uncovering new material functionalities.
• The CALYPSO methodology provides a robust framework for advancing CSP.