Computational materials science
[1] These computer simulation methods use underlying models and approximations to understand material behavior in more complex scenarios than pure theory generally allows and with more detail and precision than is often possible from experiments.
[3] Major current themes in the field include uncertainty quantification and propagation throughout simulations for eventual decision making, data infrastructure for sharing simulation inputs and results,[4] high-throughput materials design and discovery,[5] and new approaches given significant increases in computing power and the continuing history of supercomputing.
Many variations of electronic structure methods exist of varying computational complexity, with a range of trade-offs between speed and accuracy.
Due to its balance of computational cost and predictive capability density functional theory (DFT) has the most significant use in materials science.
Within DFT there are increasingly complex, accurate, and slow approximations underlying the simulation because the exact exchange-correlation functional is not known.
The term Molecular dynamics (MD) is the historical name used to classify simulations of classical atomic motion through time.
Typically, interactions between atoms are defined and fit to both experimental and electronic structure data with a wide variety of models, called interatomic potentials.
The forces for MD can also be calculated using electronic structure methods based on either the Born-Oppenheimer Approximation or Car-Parrinello approaches.
More recent efforts strive for robust, transferable models with generic functional forms: spherical harmonics, Gaussian kernels, and neural networks.
Because there is no restriction of directly integrating motion (as in molecular dynamics), kMC methods are able to simulate significantly different problems with much longer timescales.
Plastic deformation in metals is dominated by the movement of dislocations, which are crystalline defects in materials with line type character.
[8][9] The drawback to 2D DDD simulations is that phenomena involving movement out of a glide plane cannot be captured, such as cross slip and climb, although they are easier to run computationally.
Both the free energy function and the kinetics (mobilities) are defined in order to propagate the interfaces within the system through time.
Finite element methods divide systems in space and solve the relevant physical equations throughout that decomposition.
[1] Many of the methods described can be combined, either running simultaneously or separately, feeding information between length scales or accuracy levels.
[2] Hierarchical simulation refers to those which directly exchange information between methods, but are run in separate codes, with differences in length and/or time scales handled through statistical or interpolative techniques.
The most common scenario for classical molecular dynamics simulations is to develop the interatomic model directly using density functional theory, most often electronic structure calculations.
Examples include Quantum ESPRESSO (DFT), LAMMPS (MD), ParaDIS (DD), FiPy (phase field), and MOOSE (Continuum).
