.. _Pandat HTC: https://computherm.com/htc
.. _PanOptimizer: https://computherm.com/panoptimizer

Features of PanPython SDK
=========================

Parallel High-Throughput-Calculation (HTC)
---------------------------------------------

The **High-Throughput-Calculation (HTC)** function is a build-in function of Pandat
software which allows a user to explore materials properties by performing a batch
of calculations under user-defined conditions. The purpose of HTC is to quickly scan
a composition and/or temperature space and discover alloy compositions and processing
conditions that meet user-defined criteria for certain properties through data mining
of the HTC results. The HTC function is significantly enhanced through **PanPython SDK**,
which allows a user to perform parallel HTC. The HTC results can be visualized and
analyzed using interactive Jupyter notebook.

See demos of HTC through PanPython SDK

 - :ref:`c_python_htc_points-label`
 - :ref:`c_python_htc_precip-label`
 - :ref:`pandat_htc_point-label`

HTC function is also available through Pandat GUI. Please see `Pandat HTC`_ for details.

Optimization of Kinetic Model Parameters
----------------------------------------

In addition to a thermodynamic and mobility database, precipitation simulation also requires
a precipitation data file (.KDB) which provides kinetic model parameters, such as interfacial
energy, molar volume, and nucleation site parameter. Since kinetic model parameters are
alloy-dependent, they need to be calibrated using experimental data available to the alloy under
investigation. Due to the co-play of these kinetic parameters, it is essential to optimize them
altogether with assistance from regression analysis software.

The **PanPython SDK** provides a solution to automatic optimize multiple kinetic parameters.
It is powerful since it can handle any model parameters defined in the flexible XML format.
In the following example, interfacial energy and nucleation site parameter are optimized
simultaneously for aluminum alloys AA6xxx using experimental data including time-dependent
particle size, particle number density, and hardness.


 - :ref:`python_kopt-label`


Fast Post-Processing of HTC Data
--------------------------------

Materials design relies on property data generated by experimental measurements or/and
simulations. While aggregating data sets from various sources is the first step which
is crucial for further data visualization and analysis, it is always challenging to handle
data sets of different types. **PanPython SDK** can provide solutions to this challenge.
In the following examples, **PanPython SDK** is used to process variety types of data
generated by Pandat HTC function.

 - :ref:`pp_python_htc_point-lable`
 - :ref:`pp_pandat_htc_precip-label`

Interactive Analysis of Materials Properties
--------------------------------------------

Data analysis and display plays an essential role in correctly understanding and using the data.
Modern material design usually adopts multiple components to tailor material properties. Display
of numerous material properties properly for a multi-component and multi-phase system is challenging.
**PanPython SDK** can provide a solution to this challenge. It allows a user to visualize and
analyze the multi-dimensional data sets. In addition, **PanPython SDK** is also conveniently
connected with the state-of-art Python packages such as SciPy, pandas and Scikit-learn, which
provides a rich variety of data analysis functions.

In the following demos, Jupyter notebooks are provided to analyze and visualize results from HTC.

 - :ref:`pp_python_htc_point-lable`
 - :ref:`pp_pandat_htc_precip-label`