Computational materials design of crystalline solids.

Area of Science:

• Computational materials science
• Materials informatics
• Materials design

Background:

• First-principles modeling is increasingly accurate for in silico design and testing of new materials.
• Computational materials science is crucial for developing novel functional materials and composites for next-generation technologies.
• Simulation techniques are applied to materials for energy generation, storage, and conversion.

Purpose of the Study:

• To explore the development process of computational materials design.
• To critically review progress in thermoelectrics and photovoltaics using computational methods.
• To advance universal chemical-design principles.

Main Methods:

• Crystal-structure prediction (global optimization)
• Data mining (materials informatics)
• High-throughput screening
• Machine learning
• Simulation of lattice thermal conductivity

Main Results:

• Demonstrated the integration of various simulation techniques for materials design.
• Reviewed progress in thermoelectrics and photovoltaics, including Pb-free hybrid halide perovskites.
• Advanced universal chemical-design principles.

Conclusions:

• In silico design and testing using computational materials science are vital for next-generation technologies.
• Integrated simulation approaches, including machine learning, are key to accelerating materials discovery.
• Established chemical-design principles guide the development of novel functional materials.