研究目的
To review recent progress in discovering and developing new functional materials using high-throughput computational materials design, focusing on the rational design of screening procedures and development of materials descriptors for electronic and magnetic properties in nanoelectronics applications.
研究成果
High-throughput computational materials design, aligned with the materials genome approach, accelerates the discovery of functional materials by integrating computations, experiments, and digital data. Key successes include identifying novel materials for thermoelectrics, topological insulators, transparent conducting oxides, 2D electron gas systems, halide perovskites, light-emitting diodes, and magnets. Future work should focus on improving descriptors and combining mechanism-driven and data-driven strategies.
研究不足
The review highlights challenges in developing effective materials descriptors due to the complexity of physical problems, limitations of computational techniques in producing accurate materials information, and the reliance on available digital data. Machine learning approaches are noted for their limited accuracy. Experimental validation is required for predicted materials, which may not always be feasible.
1：Experimental Design and Method Selection:
The methodology involves high-throughput computational materials design, utilizing first-principles calculations (e.g., density functional theory) to build quantum materials repositories and screen for materials with desired properties. It includes developing materials descriptors and screening algorithms.
2：Sample Selection and Data Sources:
Samples include inorganic crystalline materials from databases such as AFLOWLIB, Materials Project, and Inorganic Crystal Structure Database (ICSD), as well as hypothetical materials generated based on structural prototypes.
3：List of Experimental Equipment and Materials:
Computational tools and software frameworks are used, including AFLOW, pymatgen, Atomic Simulation Environment (ASE), and MatCloud. No physical equipment is mentioned; the work is computational.
4：Experimental Procedures and Operational Workflow:
The workflow involves generating input structures, performing high-throughput electronic structure calculations, processing and storing materials data, and screening for target materials using descriptors. Specific steps include structural relaxations, band structure calculations, and defect formation energy calculations.
5：Data Analysis Methods:
Data analysis employs statistical techniques and algorithms for screening, such as convex hull analysis for stability, and uses software tools for quantum chemistry calculations and machine learning in some cases.
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