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Updated: Dec 20, 2025

On-Chip Crystallization and Large-Scale Serial Diffraction at Room Temperature
Published on: March 11, 2022
The CRYSTAL code, 1976-2020 and beyond, a long story
Roberto Dovesi1, Fabien Pascale2, Bartolomeo Civalleri1
1Dipartimento di Chimica, and Centre of Excellence NIS (Nanostructured Interfaces and Surfaces), Università di Torino, via Giuria 5, I-10125 Torino, Italy.
CRYSTAL is a versatile periodic ab initio code for electronic structure calculations. It efficiently handles various dimensionalities and properties, offering scalable parallel versions for high-performance computing.
Area of Science:
- Computational Physics
- Quantum Chemistry
- Materials Science
Background:
- Periodic ab initio calculations are crucial for understanding crystalline materials.
- Accurate electronic structure methods are needed for predicting material properties.
- Efficient and scalable computational codes are essential for modern research.
Purpose of the Study:
- To introduce CRYSTAL, a periodic ab initio code for electronic structure calculations.
- To highlight its capabilities in handling diverse systems and properties.
- To emphasize its parallel implementations for high-performance computing.
Main Methods:
- Utilizes Gaussian-type basis sets for crystalline orbitals (Bloch functions).
- Supports all-electron and pseudopotential calculations with various density functionals, including Hartree-Fock.
- Fully exploits symmetry and offers tools for structural modification and property evaluation.
Main Results:
- CRYSTAL treats 3D, 2D, 1D, and 0D systems uniformly.
- Enables calculation of numerous tensorial properties and vibrational spectra (IR, Raman) with analytical intensities.
- Provides serial, parallel, and massive-parallel versions for efficient execution on HPC clusters.
Conclusions:
- CRYSTAL is a powerful, flexible, and scalable code for electronic structure studies.
- Its comprehensive features facilitate the investigation of complex materials and properties.
- Efficient parallelization ensures its applicability on large-scale computing architectures.
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