Materials Design On-the-Fly.

Area of Science:

• Materials Science
• Computational Chemistry
• Solid-State Physics

Background:

• Predicting new materials with specific properties from first principles is a long-standing goal in solid-state theory.
• High-throughput calculations have advanced material discovery but still require experimental crystal structure data.
• Current methods necessitate experimental input, limiting purely theoretical material design.

Purpose of the Study:

• To develop a computational method for predicting and optimizing material properties from scratch.
• To eliminate the need for experimental crystal structure data in material property prediction.
• To demonstrate a general approach for designing materials with tailored properties.

Main Methods:

• Combined global structure prediction methods to determine ground-state crystal structures.
• Utilized an evolutionary algorithm to optimize chemical composition for desired properties.
• Applied the method to an unbiased search for superhard materials and transparent conductors.

Main Results:

• Successfully predicted material properties using only fundamental laws and the periodic table.
• Demonstrated the capability to optimize for specific properties like superhardness and transparency.
• Showcased the method's generality for optimizing any calculable material property.

Conclusions:

• A novel computational framework enables *ab initio* material property optimization.
• Experimental crystal structure determination is no longer a prerequisite for designing new materials.
• The developed method offers a powerful, general tool for accelerating materials discovery.