Tuning and interpretation of electronic transport properties with [Formula presented]

Electronic transport coefficients such as the electrical conductivity, the termo-power, and the charge carrier concentration are routinely measured in a variety of application areas such as electronics or thermoelectric power-generation and cooling. Using fitting procedures, those measurements are used to infer microscopic features of the samples. The code [Formula presented] facilitates the estimation of electronic structure parameters such as the effective masses and band energies improving the current approach by increasing the complexity of the band structure representation. The software is designed with a server-client architecture to enhance performance and scalability. The server is implemented in the Julia programming language. We illustrate the design and the efficiency of the code with selected applications, which are relevant to the optimization of thermoelectric materials. Program summary: Program Title: [Formula presented] CPC Library link to program files: https://doi.org/10.17632/494nj6ftbv.1 Developer's repository link: https://github.com/marcofornari/etransport.git Licensing provisions: GPLv3 Programming language: Julia, Python. Nature of problem: Electronic transport measurements are routinely used to link the functional properties exploited in electronics and energy sciences applications with the underlying electronic structure of the material under investigation. Experimentalists collect data of derived and integrated physical quantities, from which they estimate properties and parameters, e.g., nature of the conduction mechanisms and effective mass. The interpretation is based on simplistic models that rarely represent the complexity of the band structure. The code [Formula presented] improves the current state of the art and facilitates the estimation of effective masses and energies from experimental measurements of electrical conductivity, Seebeck coefficients, and carrier concentration using a multi-valley anisotropic parabolic band structure as a model for the unknown real band structure. Experimental data can be imported and visualized to facilitate the comparison with theoretical analysis. Solution method: The Boltzmann equation within the relaxation-time approximation is used to compute the electronic transport tensors from the electronic density of states characterized in terms of a set of effective masses tensors m_i^⁎ and critical energies ϵ_i. Numerical and analytical techniques are adopted to integrate the Boltzmann equation efficiently. By tuning the features of the density of states, temperature, Fermi level, and the functional expression for the relaxation time, the software streamlines the reconstruction of the underlying electronic properties from experimental data. Additional comments including restrictions and unusual features: The software is designed with a server-client architecture to enhance performance and scalability. The server is implemented in the Julia programming language as a stand-alone library. It performs all the numerical operations to compute the transport tensors from a set of multi-valley parabolic band structures. The client is implemented either as a command-line user interface (CLI) or a graphical user interface (GUI) written in the Python programming language. The communications between the server and clients are handled by a REST API, where the data are exchanged using JSON payloads.