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Browse filesUpdated description of MACE foundation model.
README.md
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## License summary
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1. The Licensed Models are **only** available under this License for Non-Commercial Purposes.
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# MACE
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## Reference
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Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, and Gábor Csányi.
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Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,
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2023. URL: https://arxiv.org/abs/2206.07697.
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## How to Use
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For complete usage instructions, please refer to our [documentation](https://instadeep.github.io/mlip)
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## Model architecture
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| Parameter | Value | Description |
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|---------------------------------|----------------------|-----------------------------------------------------|
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| `num_layers` | `2` | Number of MACE layers. |
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| `num_channels` | `128` | Number of channels. |
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| `l_max` | `3` | Maximal degree of spherical harmonics. |
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| `node_symmetry` | `1` | Highest degree of node features. |
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| `correlation` | `3` | Maximum correlation order. |
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| `readout_irreps` | `["16x0e", "0e"]` | Irreps for the readout block. |
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| `num_readout_heads` | `1` | Number of readout heads. |
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| `include_pseudotensors` | `false` | Whether all parities are kept. |
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| `num_bessel` | `8` | Number of Bessel basis functions. |
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| `activation` | `silu` | Activation function in the non-linear readout block.|
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| `radial_envelope` | `polynomial_envelope`| Radial envelope function. |
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| `symmetric_tensor_product_basis`| `false` | Whether to use a symmetric tensor product basis. |
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| `atomic_energies` | `average` | Treatement of atomic energies. |
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| `avg_num_neighbors` | `null` | Mean number of neighbors. |
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For more information about MACE hyperparameters,
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please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/models/mace.html#mlip.models.mace.config.MaceConfig)
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## Training
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Training is performed over 220 epochs, with an exponential moving average (EMA) decay rate of 0.99.
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The model employs a Huber loss function with scheduled weights for the energy and force components.
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Initially, the energy term is weighted at 40 and the force term at 1000.
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At epoch 115, these weights are flipped.
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We use our default MLIP optimizer in v1.0.0 with the following settings:
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| Parameter | Value | Description |
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|----------------------------------|---------|-----------------------------------------------------|
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| `init_learning_rate` | `0.01` | Initial learning rate. |
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| `peak_learning_rate` | `0.01` | Peak learning rate. |
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| `final_learning_rate` | `0.01` | Final learning rate. |
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| `weight_decay` | `0` | Weight decay. |
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| `warmup_steps` | `4000` | Number of optimizer warm-up steps. |
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| `transition_steps` | `360000`| Number of optimizer transition steps. |
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| `grad_norm` | `500` | Gradient norm used for gradient clipping. |
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| `num_gradient_accumulation_steps`| `1` | Steps to accumulate before taking an optimizer step.|
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For more information about the optimizer,
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please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/training/optimizer.html#mlip.training.optimizer_config.OptimizerConfig)
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## Dataset
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| Parameter | Value | Description |
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|-----------------------------|-------|--------------------------------------------|
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| `graph_cutoff_angstrom` | `5` | Graph cutoff distance (in Å). |
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| `max_n_node` | `32` | Maximum number of nodes allowed in a batch.|
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| `max_n_edge` | `288` | Maximum number of edges allowed in a batch.|
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| `batch_size` | `64` | Number of graphs in a batch. |
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This model was trained on the [SPICE2_curated dataset](https://huggingface.co/datasets/InstaDeepAI/SPICE2-curated).
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For more information about dataset configuration
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please refer to our [documentation](https://instadeep.github.io/mlip/api_reference/data/dataset_configs.html#mlip.data.configs.GraphDatasetBuilderConfig)
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## License summary
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1. The Licensed Models are **only** available under this License for Non-Commercial Purposes.
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