Release Notes
===============

Mdapy 1.0.0 (January 3, 2026)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

🚀 Summary
----------
This is a milestone update for **mdapy**, featuring a near-complete rewrite of the core architecture. A primary driver for this transition was the limitations of our previous JIT dependency, **Taichi**; its development pace constrained our support for newer Python versions.

To ensure long-term sustainability, we have reconstructed mdapy by migrating computationally intensive kernels to C++ using the modern **nanobind** wrapper. As a result, mdapy now depends solely on **NumPy** and **Polars**, making it exceptionally lightweight and compatible with all modern Python environments.

**Key changes include:**

* **Engine Shift:** With the removal of Taichi, mdapy now focuses on high-performance CPU computation.
* **GUI & Tools:** Experimental Polyscope support has been removed to focus on core stability. However, a lightweight Jupyter-based GUI remains available as an optional dependency.
* **Modern Build System:** We have transitioned from ``setup.py`` to ``pyproject.toml``.
* **Reliability:** Extensive test suites have been added to ensure the correctness of all algorithms.

This is a brand-new foundation for the project, and we strongly recommend all users to upgrade.

🏆 New features
--------------
* **qNEP Integration:** Support for evaluating energy, force, virial, charge, and BEC properties.
* **Structural Analysis:** Added Static Structure Factors and the Wigner-Seitz method for point defect detection.
* **Mechanical Properties:** Support for calculating elastic constants.
* **I/O Enhancements:** Added XYZ trajectory loading and introduced the **MP file format** (Parquet-based), providing high-speed I/O and efficient storage.
* **Minimization:** Improved FIRE2 method for energy minimization with cell optimization.
* **GPUMD Ecosystem:** A series of new features compatible with the GPUMD economy.

🛠️ Other improvements
--------------------
* **Compatibility:** Full support for Python >= 3.9.
* **Reliability:** Significantly expanded test case coverage.
* **Efficiency:** Optimized import overhead; ``import mdapy`` is now significantly faster.

⚠️ Limitations
--------------
* **Documentation:** The documentation is currently being updated and is not yet complete. We are actively working on this and welcome any community contributions or feedback.