Breaking changes

Scheduler changed to step every iteration (#234). If your config specifies lr_milestones or warmup_epochs in epochs instead of steps, this will change how your scheduler behaves.
OCPCalculator no longer takes in a Trainer class as input. Instead, a yaml file and checkpoint path must be provided.

Major features

GemNet-dT implementation and pretrained model (following https://arxiv.org/abs/2106.08903). This is now the best-performing model across all OCP tasks (see leaderboards).
SpinConv implementation and pretrained model (following https://arxiv.org/abs/2106.09575)
DimeNet++ pretrained models (#200), updated IS2RE models (#203)
~2x reduction in GPU memory usage during inference (#199, #201)
Per-adsorbate trajectory datasets (#225)
Rattled / MD datasets (#224)
IS2RE relaxation scripts; relevant for NeurIPS ‘21 challenge participants (#253)

Support for Python 3.8, PyTorch 1.8.1 (#247)
Early stopping for ML relaxations
Support for gradient clipping and maintaining an exponential moving average of parameters
Preprocessing support for datasets that do not have fixed atoms specified (#189)
Jupyter notebooks for creating LMDBs on your own data, understanding the data preprocessing pipeline (#211)
Release cached CUDA memory after each relaxation batch (#190)
Security fix in loading EvalAI npz submissions (#194)
Dataloader bug fix for when #GPUs > #LMDBs (#248)
Support for custom optimizers (#218)
Support for custom schedulers (#226)
New attributes (miller_index, shift, adsorption site, etc.) in data mapping (#219)
Deterministic unit tests (#228)
Bug fixes in released data for all tasks / splits (#197)
Improved logs: using logging instead of print commands, recording slurm settings
Better handling of job resumption on pre-emption; particularly relevant for those using slurm-based clusters. Model weights and training state are now saved to separate checkpoints, and all restarted jobs log to the same wandb plot instead of a new plot per restart.
Support for energy-only predictions in OCPCalculator (#243)