Density Functional Theory (DFT) in Crystalline Material
A special issue of Crystals (ISSN 2073-4352).
Deadline for manuscript submissions: 10 February 2026 | Viewed by 840
Special Issue Editors
Interests: DFT simulations; electronic structure; material modeling; computational physics
Interests: quantum chemistry; DFT applications; monte carlo/molecular dynamics simulations; material science
Special Issue Information
Dear Colleagues,
The density functional theory (DFT) has become a cornerstone in computational materials science, providing insights into the electronic, structural, and thermodynamic properties of crystalline materials. With advancements in computational power and algorithms, DFT enables accurate predictions of material behavior, facilitating innovations in energy storage, catalysis, semiconductors, and beyond. This Special Issue of Crystals aims to highlight recent breakthroughs and challenges regarding the application of DFT to crystalline systems, thereby fostering interdisciplinary collaboration among theorists and experimentalists.
This Special Issue seeks to publish state-of-the-art research and reviews on DFT applications in crystalline materials, aligning with the journal scope of computational and theoretical materials science. We encourage contributions that address methodological advancements, novel applications, or the validation of DFT predictions against experimental data.
The topics of interest for this Special Issue are as follows:
- DFT methodologies for crystalline systems (e.g., hybrid functionals, van der Waals corrections, etc.).
- Electronic structure and bandgap engineering in semiconductors and insulators.
- Defects and doping in crystalline materials (e.g., point defects and dislocations).
- Phase stability and thermodynamic properties of crystalline phases.
- Mechanical properties (elastic constants, hardness, and fracture mechanisms).
- Surface and interface phenomena (catalysis, adsorption, and heterostructures).
- High-throughput DFT screening for materials discovery.
- Machine learning-assisted DFT for accelerated materials design.
- Validation of DFT predictions with experimental results (XRD, spectroscopy, etc.).
We look forward to your contributions.
Prof. Dr. Tarciso Silva Andrade-Filho
Prof. Dr. Rodrigo Gester
Prof. Dr. Mateus Ribeiro Lage
Guest Editors
Manuscript Submission Information
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Keywords
- density functional theory
- crystalline materials
- electronic structure
- defects
- phase stability
- mechanical properties
- surface science
- high-throughput screening
- machine learning
- computational material science
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