Package announcement: Pymablock
Hello fellow Kwanters, I am happy to announce Pymablock, a Python package useful to combine with Kwant. Its main purpose is constructing effective models using perturbation theory, which may help you in different ways. Pymablock allows you to analyse the behavior of a few levels of a large Hamiltonian, or to construct a symbolic k.p model. We originally wrote the code for one of our projects, but then saw that this tool is useful almost for the entire group, so we decided to turn it into a package. We spent countless hours generalizing, optimizing, and testing it so that it is efficient and reliable. Now it handles multiple perturbations to any order, and it is compatible with symbolic and numerical calculations. We hope that Pymablock will save you a lot of effort and computation time. To see where it can help, check out our its documentation (https://pymablock.rtfd.io/), and in particular the tutorials about the k.p model of bilayer graphene (https://pymablock.readthedocs.io/en/latest/tutorial/bilayer_graphene.html) or the induced superconducting gap (https://pymablock.readthedocs.io/en/latest/tutorial/induced_gap.html). Just like Kwant, Pymablock is open source (https://gitlab.kwant-project.org/qt/pymablock), and you are welcome to follow and contribute to its development. Let us know if you have any questions or comments! Also if there's interest we can organize an online introduction of the package Best, Anton Akhmerov and the Pymablock maintainers: Isidora Araya Day and Sebastian Miles
Hello everyone, I have an update: if you want to know more about pymablock, we will be giving an online talk about it. We will explain the algorithms we developed, show the package capabilities in a tutorial, and of course we'll be happy to answer your questions! The talk will take place Wednesday Nov 15 at 17:00 EU time / 11:00 Eastern time. The details are here: https://virtualscienceforum.org/speakers-corner/#pymablock-a-software-packag... Best, Anton (on behalf of pymablock maintainers) On Tue, 20 Jun 2023 at 16:22, Anton Akhmerov <i@antonakhmerov.org> wrote:
Hello fellow Kwanters,
I am happy to announce Pymablock, a Python package useful to combine with Kwant. Its main purpose is constructing effective models using perturbation theory, which may help you in different ways. Pymablock allows you to analyse the behavior of a few levels of a large Hamiltonian, or to construct a symbolic k.p model.
We originally wrote the code for one of our projects, but then saw that this tool is useful almost for the entire group, so we decided to turn it into a package. We spent countless hours generalizing, optimizing, and testing it so that it is efficient and reliable. Now it handles multiple perturbations to any order, and it is compatible with symbolic and numerical calculations. We hope that Pymablock will save you a lot of effort and computation time. To see where it can help, check out our its documentation (https://pymablock.rtfd.io/), and in particular the tutorials about the k.p model of bilayer graphene (https://pymablock.readthedocs.io/en/latest/tutorial/bilayer_graphene.html) or the induced superconducting gap (https://pymablock.readthedocs.io/en/latest/tutorial/induced_gap.html).
Just like Kwant, Pymablock is open source (https://gitlab.kwant-project.org/qt/pymablock), and you are welcome to follow and contribute to its development.
Let us know if you have any questions or comments! Also if there's interest we can organize an online introduction of the package
Best, Anton Akhmerov and the Pymablock maintainers: Isidora Araya Day and Sebastian Miles
participants (3)
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Anton Akhmerov -
jorge huamani -
Shihao Bi