The use of linear combination of numerical atomic orbitals makes SIESTA a flexible an efficient DFT code. SIESTA is able to produce very fast calculations with small basis sets, allowing computing systems with a thousand of atoms. At the same time, the use of more complete and accurate bases allows to achieve accuracy comparable to those of standard plane waves calculations, still at an advantageous computational cost.
The characteristics of DFT code SIESTA are:
- It uses the standard Kohn-Sham self-consistent density functional method in the local density (LDA-LSD) or generalized gradient (GGA) approximations.
- It uses norm-conserving pseudopotentials in their fully nonlocal (Kleinman-Bylander) form.
- It uses atomic orbitals as a basis set, allowing unlimited multiple-zeta and angular momenta, polarization and on-site orbitals. Finite-support basis sets are the key for calculating the Hamiltonian and overlap matrices in O(N) operations.
- It projects the electron wavefunctions and density onto a real-space grid in order to calculate the Hartree and exchange-correlation potentials and their matrix elements.
- Besides the standard Rayleigh-Ritz eigenstate method, it allows the use of localized linear combinations of the occupied orbitals (valence-bond or Wannier-like functions), making the computer time and memory scale linearly with the number of atoms. Simulations with several hundred atoms are feasible with modest workstations.
- It is written in Fortran 95 and memory is allocated dynamically. It may be compiled for serial or parallel execution (under MPI).
SIESTA (Spanish Initiative for Electronic Simulations with Thousands of Atoms) is both a method and its computer program implementation, to perform efficient electronic structure calculations and ab initio molecular dynamics simulations of molecules and solids.
Information and credits on: http://www.icmab.es/siesta/
1.1.- Ab initio quantum mechanical (or first principle) models
- Hartree-Fock (HF) method
- Higher level ab initio method
- Density Funcional Theory
1.2.- Many body models and effective Hamiltonians
- Nearly-free electron models
- Pseudopotentials
- Semi-empirical tigh binding potential (TB) model
- Hubbard model
- k·p effective Hamiltonian
- Polarisable continuum model
- Envelope function approximation for continuous media
1.3.- Quantum mechanical in response to time dependent fields
- TD-DFT and TD(Spin)DFT
- The time-dependent
- k·p-model
- Other time dependent models
1.4.- Statistical charge transport model
- Semi-classical drift-diffusion model
- Percolation models
2.1.-Molecular Mechanics
2.2.- Statistical Mechanics models: Molecular Dynamics (MD)
- Cassical Molecular Dynamics
- Ab initio molecular dynamics
- Quantum mechanics/molecular mechanics (QM/MM)
2.3.- Statistical Mechanics models: Monte Carlo molecular models
2.4.- Atomistic Spin models
2.5.-Statistical Mechanics for atomistic systems
- Langevin Dynamic method for magnetic spin systems
- Semi-classical non-equilibrium spin transport model
- Statistical transport model at atomistic level
Ref. L. Rosso; A.F. de Baas (2012), “What makes a material function? Let me compute the ways…”
SIMUNE is working on offering a wide range of softwares able to best fits the needs of our customer. In particular, SIMUNE is expert with the SIESTA package and it is currently working together with the SIESTA developers to improve and extend the code capabilities.
In the market there are several software-packages available for a given application; but it is difficult to find a package that is useful for all the simulations typically needed prior an experimental phase and the manufacturing process. SIMUNE offers the right tool for each project, as well as the assessment/advice of the scientist that best fits the project, thanks to our international network.
The following are examples of properties that can be calculated by simulations:
- Calculation of electronic states
- Thermodynamics
- Electronic and ionic transport
- Electrical conductivity
- Electrochemical mechanisms
- Catalytic surfaces
- Optical processes
- Energy transfer within and among molecules
- Phonons: collective excitations
- Corrosion
- Novel materials
These are examples where SIMUNE´s simulations can be applied:
- Graphene and carbon nanotubes
- Nanowires
- Magnetic tunnel junctions
- Molecular electronics
- Complex interfaces
- High-k dielectrics
- Spintronics
- Single-electron transistors
- Electrocatalysis
- Photovoltaics and semiconductors
- Batteries:
- Surface modification
- Post-mortem and failure analysis
- Electrochemical models
- Hybrid coatings
- Characterization
- Porous carbon electrodes for electrochemical capacitors and batteries
- Corrosion protection
- Flexible electronics
- Sensing
- Quantum dots
- Nanocomposites
- etc
Experimental testing is usually an expensive task. Simulations can reduce this cost dramatically by using computational resources. Through simulations it is possible, for example, to predict the properties of materials before they exist and to understand the behavior of materials atom by atom.
By using computing simulation you can:
- Save costs by identifying new materials for new products
- Reduce time-to-market of novel materials and R&D acceleration
- Understand results of measurements
- Provide guidelines to design effective materials for specific applications
- Analysis and characterisation of material properties that are not accesible experimentally
- Study physical and chemical phenomena for every point in the sample at every time
A simulation is a computational tool that substitutes and complements the experimental test-error strategy. These simulations save costs and time that otherwise should be spent in experimental testing. Simulations allow (i) to determine the ideal conditions that are needed to optimize the experimental process, (ii) to establish the parameters that would improve the quality of, for example, materials deposition, and (iii) to predict final distributions depending on the entry parameters.