v0.19.0

[0.19.0]

Added

• nequip-package list: list the contents (with file sizes) of a packaged model file
• nequip-package diff: diff a file inside a package against its installed or a local version; useful to check for code drift between the packaged snapshot and the currently installed libraries
• nequip-package update: replace specific files inside a package and verify that model predictions are unchanged; intended for applying bug-fixes or compatibility patches without retraining
• nequip-package modify: apply one or more persistent model modifiers to a packaged model (e.g. adjust per-type energy scales/shifts, toggle force/stress output)
• per-species force loss weighting: per_type_coeffs key on MetricsManager metrics, and per_type_forces_coeffs kwarg on EnergyForceLoss and EnergyForceStressLoss for emphasizing some atom types over others in the forces loss (weighted-mean aggregation over per-type MSEs) by @DavidW99
• AOTI-packaged models now record the custom-op libraries their GPU kernels require (e.g. openequivariance) and import them automatically at load time, so packaged models with custom GPU kernels can be loaded in plain Python without first manually importing the kernel library (only applies to models packaged with this version or later; previously packaged models do not carry this metadata and still require manual import)

Changed

• [Breaking] MetricsManager internal refactor: the metrics attribute (a dict of dicts) has been replaced by entries (a dict of MetricEntry dataclasses). Existing checkpoints are incompatible. Callbacks and code that accessed loss.metrics[name]["coeff"] must be updated to loss.entries[name].coeff; affected built-in callbacks: SoftAdaptCallback, LossCoeffScheduler, LossCoeffMonitor.
• [Breaking] minimum lightning requirement raised to >=2.6 (from >=2.0); environments pinned to older lightning must upgrade. Checkpoint loading now passes weights_only=False to accommodate lightning 2.6's new weights_only=True default

Fixed

• #601
• silent wrong forces/stress in the torch-sim integration: non-contiguous input tensors (e.g. the transposed row_vector_cell from torch_sim.concatenate_states/BinningAutoBatcher, or stride-0 expanded PBC) were read by AOTI models using assumed strides; all model inputs are now made contiguous
• double-backward overflow in large models: silu_backward is now evaluated via the torch.sigmoid primitive (instead of the 1/(1+exp(-x)) sigmoid expansion) through a patched AOTI decomposition table by @kavanase