v0.5.0 (#390)

Author: orionarcher

CHANGELOG.md

@@ -1,6 +1,27 @@
 <!-- markdownlint-disable -->
 # Changelog
 
+## v0.5.0
+
+This release focuses on improving batch processing capabilities across TorchSim. The neighbor list module has been completely refactored to support batched calculations with multiple backend implementations, elastic tensor calculations now leverage batched operations for improved performance, and a bug fix ensures Monte Carlo swaps work correctly with ragged (different-sized) systems.
+
+### 🎉 New Features
+* Refactor neighbor list module with batched support and multiple backends by @abhijeetgangan in [#348](https://github.com/TorchSim/torch-sim/pull/348)
+  - New unified `torchsim_nl` function with automatic backend selection
+  - Multiple implementations: Alchemiops (NVIDIA CUDA), Vesin, torch_nl, and pure PyTorch fallback
+  - Support for both single-system and batched (multi-system) calculations
+  - Automatic selection of best available implementation based on installed packages
+
+### 🛠 Enhancements
+* Batch elastic operations by @orionarcher in [#384](https://github.com/TorchSim/torch-sim/pull/384)
+  - `calculate_elastic_tensor` now uses `ts.static` runner for batched calculations
+  - Added `autobatcher` parameter for memory-efficient processing of deformations
+  - Added `pbar` parameter for progress bar support
+
+### 🐛 Bug Fixes
+* Fix Monte Carlo swap for ragged systems by @curtischong in [#380](https://github.com/TorchSim/torch-sim/pull/380)
+  - Fixed `generate_swaps` calculation of system start indices for systems with different atom counts
+
 ## v0.4.2
 
 Thank you to everyone who contributed to this release! This release includes important bug fixes and new features. @thomasloux, @orionarcher, @WillEngler, @RishikeshMagar, @nh-univie, @andrewrm98, @danielzuegner, and others made valuable contributions. 🚀

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "torch-sim-atomistic"
-version = "0.4.2"
+version = "0.5.0"
 description = "A pytorch toolkit for calculating material properties using MLIPs"
 authors = [
     { name = "Abhijeet Gangan", email = "[email protected]" },