Computational Materials Chemistry: Methods and Applications
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 1150
Special Issue Editor
Interests: OLED material; phosphorescent material; metal complex; DFT; molecular modeling
Special Issue Information
Dear Colleagues,
Computational materials chemistry plays a crucial role in accelerating new materials’ discovery and development by providing insights into material properties and behaviors through computational modeling and simulations. It helps guide experimental efforts to optimize material designs, and explore new materials with tailored functionalities, leading to advancements in various fields such as energy, electronics, and materials science. The methods used in computational materials chemistry need to be selected based on the system and properties, and molecular dynamics (MD) and quantum mechanics (QM) are commonly used methods in computational materials chemistry. In particular, density functional theory (DFT) is widely employed in computational materials chemistry due to its versatility and efficiency. In recent years, with the development of computer technology, machine learning methods have become promising tools that utilize large amounts of experimental and computational data to construct models and algorithms capable of predicting material properties, discovering new materials, and accelerating the materials’ design process. This Special Issue focuses on the methods and applications of computational materials chemistry, to comprehensive understanding and exploration for computational materials chemistry. We hope that this Special Issue inspires authors and readers and makes an important contribution to the field of computational materials chemistry.
Dr. Cong Zhang
Guest Editor
Manuscript Submission Information
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Keywords
- molecular modeling
- materials design
- molecular dynamics (MD)
- quantum mechanics (QM)
- density functional theory (DFT)
- machine learning
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