{"manifests":[{"schema_version":"1","cluster":"Moria","root":"/tmp/rootstock-test-2","maintainer":{"name":"Gimli, Son of Gloin","email":"nevertoss@dwarf.me"},"rootstock_version":"0.5.0","python_version":"unknown","last_updated":"2026-03-12T16:01:31.461151+00:00","environments":{"chgnet_env":{"status":"ready","built_at":"2026-03-12T16:01:17.828540+00:00","source_hash":"sha256:ee3d3997682c2b5fd44e2e602a917080ad8e29d8e107990636b77af24ecbb871","source":"# /// script\n# requires-python = \">=3.10\"\n# dependencies = [\n#     \"chgnet>=0.3.0\",\n#     \"ase>=3.22\",\n#     \"torch>=2.0\",\n# ]\n# ///\n\"\"\"\nCHGNet environment for Rootstock.\n\nThis environment provides access to CHGNet, a pretrained universal neural\nnetwork potential for charge-informed atomistic modeling.\n\"\"\"\n\n\ndef setup(model: str | None = None, device: str = \"cuda\"):\n    \"\"\"\n    Load a CHGNet calculator.\n\n    Args:\n        model: Optional path to a fine-tuned model. If None, uses the\n               default pre-trained CHGNet model.\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from chgnet.model import CHGNetCalculator\n\n    if model:\n        return CHGNetCalculator(model_path=model, use_device=device)\n    return CHGNetCalculator(use_device=device)\n","python_requires":">=3.10","dependencies":{"ase":"3.27.0","chgnet":"0.4.2","rootstock":"0.6.1","torch":"2.10.0"},"checkpoints":[]},"mace_env":{"status":"ready","built_at":"2026-03-12T16:01:23.024860+00:00","source_hash":"sha256:8f49eb565411b7d3f015383c4e5d52ca9770d4295d9cb2c51c10cfa8ab173e75","source":"# /// script\n# requires-python = \">=3.10\"\n# dependencies = [\n#     \"mace-torch>=0.3.0\",\n#     \"ase>=3.22\",\n#     \"torch>=2.4.0,<2.10\",\n# ]\n# ///\n\"\"\"\nMACE environment for Rootstock.\n\nThis environment provides access to MACE foundation models for\nmachine learning interatomic potentials.\n\nModels:\n    - \"small\", \"medium\", \"large\": Pre-trained MACE-MP-0 models\n    - Path to a .pt file: Custom fine-tuned model\n\"\"\"\n\n\ndef setup(model: str, device: str = \"cuda\"):\n    \"\"\"\n    Load a MACE calculator.\n\n    Args:\n        model: Model identifier. Can be:\n            - \"small\", \"medium\", \"large\" for MACE-MP-0 foundation models\n            - Path to a .pt file for custom models\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from mace.calculators import mace_mp\n\n    return mace_mp(model=model, device=device, default_dtype=\"float32\")\n","python_requires":">=3.10","dependencies":{"ase":"3.27.0","mace-torch":"0.3.15","rootstock":"0.6.1","torch":"2.9.1"},"checkpoints":[]},"uma_env":{"status":"ready","built_at":"2026-03-12T16:01:31.461114+00:00","source_hash":"sha256:b8446f98c4c8ad7ff12f9bf39e5658bd45a989662cfa644bf48859bf7fd821c3","source":"# /// script\n# requires-python = \">=3.10,<3.11\"\n# dependencies = [\n#     \"torch>=2.4.0\",\n#     \"fairchem-core>=2.0.0\",\n#     \"ase>=3.22\",\n#     \"torch-geometric\",\n# ]\n#\n# [tool.uv]\n# find-links = [\"https://data.pyg.org/whl/torch-2.4.0+cu121.html\"]\n# ///\n\"\"\"\nUMA (Universal Atomistic Model) environment for Rootstock.\n\nThis environment provides access to Meta's UMA foundation model\nvia the FAIRChem library.\n\nModels:\n    - \"uma-s-1p1\": UMA small model (default)\n    - Other UMA variants as released by FAIRChem\n\"\"\"\n\n\ndef setup(model: str = \"uma-s-1p1\", device: str = \"cuda\"):\n    \"\"\"\n    Load a UMA calculator.\n\n    Args:\n        model: Model identifier (e.g., \"uma-s-1p1\"). Passed directly to\n               pretrained_mlip.get_predict_unit().\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from fairchem.core import FAIRChemCalculator, pretrained_mlip\n\n    predictor = pretrained_mlip.get_predict_unit(model, device=device)\n    return FAIRChemCalculator(predictor, task_name=\"omat\")\n","python_requires":">=3.10,<3.11","dependencies":{"ase":"3.27.0","fairchem-core":"2.14.0","rootstock":"0.6.1","torch":"2.8.0","torch-geometric":"2.7.0"},"checkpoints":[]}}},{"schema_version":"1","cluster":"Test Cluster","root":"/tmp/rootstock-test","maintainer":{"name":"Hayden Holbrook","email":"hholbrook9@uchicago.edu"},"rootstock_version":"0.5.0","python_version":"unknown","last_updated":"2026-03-12T21:34:48.854245+00:00","environments":{"chgnet_env":{"status":"ready","built_at":"2026-03-12T15:32:14.864663+00:00","source_hash":"sha256:ee3d3997682c2b5fd44e2e602a917080ad8e29d8e107990636b77af24ecbb871","source":"# /// script\n# requires-python = \">=3.10\"\n# dependencies = [\n#     \"chgnet>=0.3.0\",\n#     \"ase>=3.22\",\n#     \"torch>=2.0\",\n# ]\n# ///\n\"\"\"\nCHGNet environment for Rootstock.\n\nThis environment provides access to CHGNet, a pretrained universal neural\nnetwork potential for charge-informed atomistic modeling.\n\"\"\"\n\n\ndef setup(model: str | None = None, device: str = \"cuda\"):\n    \"\"\"\n    Load a CHGNet calculator.\n\n    Args:\n        model: Optional path to a fine-tuned model. If None, uses the\n               default pre-trained CHGNet model.\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from chgnet.model import CHGNetCalculator\n\n    if model:\n        return CHGNetCalculator(model_path=model, use_device=device)\n    return CHGNetCalculator(use_device=device)\n","python_requires":">=3.10","dependencies":{"ase":"3.27.0","chgnet":"0.4.2","rootstock":"0.6.1","torch":"2.10.0"},"checkpoints":[]},"mace_env":{"status":"ready","built_at":"2026-03-12T15:32:17.538414+00:00","source_hash":"sha256:8f49eb565411b7d3f015383c4e5d52ca9770d4295d9cb2c51c10cfa8ab173e75","source":"# /// script\n# requires-python = \">=3.10\"\n# dependencies = [\n#     \"mace-torch>=0.3.0\",\n#     \"ase>=3.22\",\n#     \"torch>=2.4.0,<2.10\",\n# ]\n# ///\n\"\"\"\nMACE environment for Rootstock.\n\nThis environment provides access to MACE foundation models for\nmachine learning interatomic potentials.\n\nModels:\n    - \"small\", \"medium\", \"large\": Pre-trained MACE-MP-0 models\n    - Path to a .pt file: Custom fine-tuned model\n\"\"\"\n\n\ndef setup(model: str, device: str = \"cuda\"):\n    \"\"\"\n    Load a MACE calculator.\n\n    Args:\n        model: Model identifier. Can be:\n            - \"small\", \"medium\", \"large\" for MACE-MP-0 foundation models\n            - Path to a .pt file for custom models\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from mace.calculators import mace_mp\n\n    return mace_mp(model=model, device=device, default_dtype=\"float32\")\n","python_requires":">=3.10","dependencies":{"ase":"3.27.0","mace-torch":"0.3.15","rootstock":"0.6.1","torch":"2.9.1"},"checkpoints":[]},"uma_env":{"status":"ready","built_at":"2026-03-12T15:32:24.737016+00:00","source_hash":"sha256:b8446f98c4c8ad7ff12f9bf39e5658bd45a989662cfa644bf48859bf7fd821c3","source":"# /// script\n# requires-python = \">=3.10,<3.11\"\n# dependencies = [\n#     \"torch>=2.4.0\",\n#     \"fairchem-core>=2.0.0\",\n#     \"ase>=3.22\",\n#     \"torch-geometric\",\n# ]\n#\n# [tool.uv]\n# find-links = [\"https://data.pyg.org/whl/torch-2.4.0+cu121.html\"]\n# ///\n\"\"\"\nUMA (Universal Atomistic Model) environment for Rootstock.\n\nThis environment provides access to Meta's UMA foundation model\nvia the FAIRChem library.\n\nModels:\n    - \"uma-s-1p1\": UMA small model (default)\n    - Other UMA variants as released by FAIRChem\n\"\"\"\n\n\ndef setup(model: str = \"uma-s-1p1\", device: str = \"cuda\"):\n    \"\"\"\n    Load a UMA calculator.\n\n    Args:\n        model: Model identifier (e.g., \"uma-s-1p1\"). Passed directly to\n               pretrained_mlip.get_predict_unit().\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from fairchem.core import FAIRChemCalculator, pretrained_mlip\n\n    predictor = pretrained_mlip.get_predict_unit(model, device=device)\n    return FAIRChemCalculator(predictor, task_name=\"omat\")\n","python_requires":">=3.10,<3.11","dependencies":{"ase":"3.27.0","fairchem-core":"2.14.0","rootstock":"0.6.1","torch":"2.8.0","torch-geometric":"2.7.0"},"checkpoints":[]}}},{"schema_version":"1","cluster":"della","root":"/scratch/gpfs/ROSENGROUP/common/rootstock","maintainer":{"name":"Will Engler","email":"willengler@uchicago.edu"},"rootstock_version":"0.5.0","python_version":"3.12.12","last_updated":"2026-03-16T16:13:59.427608+00:00","environments":{"chgnet_env":{"status":"ready","built_at":"2026-03-16T16:13:58.992562+00:00","source_hash":"sha256:ee3d3997682c2b5fd44e2e602a917080ad8e29d8e107990636b77af24ecbb871","source":"# /// script\n# requires-python = \">=3.10\"\n# dependencies = [\n#     \"chgnet>=0.3.0\",\n#     \"ase>=3.22\",\n#     \"torch>=2.0\",\n# ]\n# ///\n\"\"\"\nCHGNet environment for Rootstock.\n\nThis environment provides access to CHGNet, a pretrained universal neural\nnetwork potential for charge-informed atomistic modeling.\n\"\"\"\n\n\ndef setup(model: str | None = None, device: str = \"cuda\"):\n    \"\"\"\n    Load a CHGNet calculator.\n\n    Args:\n        model: Optional path to a fine-tuned model. If None, uses the\n               default pre-trained CHGNet model.\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from chgnet.model import CHGNetCalculator\n\n    if model:\n        return CHGNetCalculator(model_path=model, use_device=device)\n    return CHGNetCalculator(use_device=device)\n","python_requires":">=3.10","dependencies":{"ase":"3.27.0","chgnet":"0.4.2","rootstock":"0.5.1","torch":"2.10.0"},"checkpoints":[]},"mace_env":{"status":"ready","built_at":"2026-03-16T16:13:59.115604+00:00","source_hash":"sha256:8f49eb565411b7d3f015383c4e5d52ca9770d4295d9cb2c51c10cfa8ab173e75","source":"# /// script\n# requires-python = \">=3.10\"\n# dependencies = [\n#     \"mace-torch>=0.3.0\",\n#     \"ase>=3.22\",\n#     \"torch>=2.4.0,<2.10\",\n# ]\n# ///\n\"\"\"\nMACE environment for Rootstock.\n\nThis environment provides access to MACE foundation models for\nmachine learning interatomic potentials.\n\nModels:\n    - \"small\", \"medium\", \"large\": Pre-trained MACE-MP-0 models\n    - Path to a .pt file: Custom fine-tuned model\n\"\"\"\n\n\ndef setup(model: str, device: str = \"cuda\"):\n    \"\"\"\n    Load a MACE calculator.\n\n    Args:\n        model: Model identifier. Can be:\n            - \"small\", \"medium\", \"large\" for MACE-MP-0 foundation models\n            - Path to a .pt file for custom models\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from mace.calculators import mace_mp\n\n    return mace_mp(model=model, device=device, default_dtype=\"float32\")\n","python_requires":">=3.10","dependencies":{"ase":"3.27.0","mace-torch":"0.3.15","rootstock":"0.5.1","torch":"2.9.1"},"checkpoints":[]},"tensornet_env":{"status":"ready","built_at":"2026-03-16T16:13:59.289290+00:00","source_hash":"sha256:df3e142f3a3199673fce02ff8683e1c6fd7343d9ca2cbc9655c6640a9cecef0f","source":"# /// script\n# requires-python = \">=3.12\"\n# dependencies = [\n#     \"torch>=2.4.0\",\n#     \"ase>=3.22\",\n#     \"matgl\",\n#     \"nvalchemi-toolkit-ops\",\n#     \"torch-geometric\",\n#     \"torch-scatter\",\n#     \"torch-sparse\",\n#     \"torch-cluster\",\n#     \"torch-spline-conv\",\n# ]\n#\n# [tool.uv]\n# find-links = [\"https://data.pyg.org/whl/torch-2.4.0+cu121.html\"]\n# ///\n\"\"\"\nTensorNet environment for Rootstock.\n\nThis environment provides access to TensorNet models via the MatGL library\nfrom the Materials Virtual Lab.\n\nModels:\n    - \"TensorNet-MatPES-PBE-v2025.1-PES\": MatPES PBE functional (default)\n    - Other MatGL models as available\n\"\"\"\n\n\ndef setup(model: str = \"TensorNet-MatPES-PBE-v2025.1-PES\", device: str = \"cuda\"):\n    \"\"\"\n    Load a TensorNet/MatGL calculator.\n\n    Args:\n        model: Model identifier (e.g., \"TensorNet-MatPES-PBE-v2025.1-PES\").\n               Passed directly to matgl.load_model().\n        device: PyTorch device string (currently MatGL handles device internally)\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    import torch\n    torch.set_default_device(device)\n\n    import matgl\n    from matgl.ext.ase import PESCalculator\n\n    pot = matgl.load_model(model)\n    return PESCalculator(potential=pot)\n","python_requires":">=3.12","dependencies":{"ase":"3.27.0","matgl":"2.0.6","nvalchemi-toolkit-ops":"0.2.0","rootstock":"0.5.1","torch":"2.8.0","torch-cluster":"1.6.3+pt24cu121","torch-geometric":"2.7.0","torch-scatter":"2.1.2+pt24cu121","torch-sparse":"0.6.18+pt24cu121","torch-spline-conv":"1.2.2+pt24cu121"},"checkpoints":[]},"uma_env":{"status":"ready","built_at":"2026-03-16T16:13:59.427557+00:00","source_hash":"sha256:b8446f98c4c8ad7ff12f9bf39e5658bd45a989662cfa644bf48859bf7fd821c3","source":"# /// script\n# requires-python = \">=3.10,<3.11\"\n# dependencies = [\n#     \"torch>=2.4.0\",\n#     \"fairchem-core>=2.0.0\",\n#     \"ase>=3.22\",\n#     \"torch-geometric\",\n# ]\n#\n# [tool.uv]\n# find-links = [\"https://data.pyg.org/whl/torch-2.4.0+cu121.html\"]\n# ///\n\"\"\"\nUMA (Universal Atomistic Model) environment for Rootstock.\n\nThis environment provides access to Meta's UMA foundation model\nvia the FAIRChem library.\n\nModels:\n    - \"uma-s-1p1\": UMA small model (default)\n    - Other UMA variants as released by FAIRChem\n\"\"\"\n\n\ndef setup(model: str = \"uma-s-1p1\", device: str = \"cuda\"):\n    \"\"\"\n    Load a UMA calculator.\n\n    Args:\n        model: Model identifier (e.g., \"uma-s-1p1\"). Passed directly to\n               pretrained_mlip.get_predict_unit().\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from fairchem.core import FAIRChemCalculator, pretrained_mlip\n\n    predictor = pretrained_mlip.get_predict_unit(model, device=device)\n    return FAIRChemCalculator(predictor, task_name=\"omat\")\n","python_requires":">=3.10,<3.11","dependencies":{"ase":"3.27.0","fairchem-core":"2.14.0","rootstock":"0.5.1","torch":"2.8.0","torch-geometric":"2.7.0"},"checkpoints":[]}}},{"schema_version":"1","cluster":"sophia","root":"/lus/eagle/projects/Garden-Ai/rootstock","maintainer":{"name":"Hayden Holbrook","email":"hholbrook@uchicago.edu"},"rootstock_version":"0.5.0","python_version":"3.10.19","last_updated":"2026-03-16T16:04:29.755973+00:00","environments":{"chgnet_env":{"status":"ready","built_at":"2026-03-16T15:59:22.114091+00:00","source_hash":"sha256:ee3d3997682c2b5fd44e2e602a917080ad8e29d8e107990636b77af24ecbb871","source":"# /// script\n# requires-python = \">=3.10\"\n# dependencies = [\n#     \"chgnet>=0.3.0\",\n#     \"ase>=3.22\",\n#     \"torch>=2.0\",\n# ]\n# ///\n\"\"\"\nCHGNet environment for Rootstock.\n\nThis environment provides access to CHGNet, a pretrained universal neural\nnetwork potential for charge-informed atomistic modeling.\n\"\"\"\n\n\ndef setup(model: str | None = None, device: str = \"cuda\"):\n    \"\"\"\n    Load a CHGNet calculator.\n\n    Args:\n        model: Optional path to a fine-tuned model. If None, uses the\n               default pre-trained CHGNet model.\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from chgnet.model import CHGNetCalculator\n\n    if model:\n        return CHGNetCalculator(model_path=model, use_device=device)\n    return CHGNetCalculator(use_device=device)\n","python_requires":">=3.10","dependencies":{"ase":"3.27.0","chgnet":"0.4.2","rootstock":"0.6.1","torch":"2.10.0"},"checkpoints":[]},"tensornet_env":{"status":"ready","built_at":"2026-03-16T15:59:22.153004+00:00","source_hash":"sha256:df3e142f3a3199673fce02ff8683e1c6fd7343d9ca2cbc9655c6640a9cecef0f","source":"# /// script\n# requires-python = \">=3.12\"\n# dependencies = [\n#     \"torch>=2.4.0\",\n#     \"ase>=3.22\",\n#     \"matgl\",\n#     \"nvalchemi-toolkit-ops\",\n#     \"torch-geometric\",\n#     \"torch-scatter\",\n#     \"torch-sparse\",\n#     \"torch-cluster\",\n#     \"torch-spline-conv\",\n# ]\n#\n# [tool.uv]\n# find-links = [\"https://data.pyg.org/whl/torch-2.4.0+cu121.html\"]\n# ///\n\"\"\"\nTensorNet environment for Rootstock.\n\nThis environment provides access to TensorNet models via the MatGL library\nfrom the Materials Virtual Lab.\n\nModels:\n    - \"TensorNet-MatPES-PBE-v2025.1-PES\": MatPES PBE functional (default)\n    - Other MatGL models as available\n\"\"\"\n\n\ndef setup(model: str = \"TensorNet-MatPES-PBE-v2025.1-PES\", device: str = \"cuda\"):\n    \"\"\"\n    Load a TensorNet/MatGL calculator.\n\n    Args:\n        model: Model identifier (e.g., \"TensorNet-MatPES-PBE-v2025.1-PES\").\n               Passed directly to matgl.load_model().\n        device: PyTorch device string (currently MatGL handles device internally)\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    import torch\n    torch.set_default_device(device)\n\n    import matgl\n    from matgl.ext.ase import PESCalculator\n\n    pot = matgl.load_model(model)\n    return PESCalculator(potential=pot)\n","python_requires":">=3.12","dependencies":{"ase":"3.27.0","matgl":"2.1.1","nvalchemi-toolkit-ops":"0.2.0","rootstock":"0.6.1","torch":"2.10.0","torch-cluster":"1.6.3+pt24cu121","torch-geometric":"2.7.0","torch-scatter":"2.1.2+pt24cu121","torch-sparse":"0.6.18+pt24cu121","torch-spline-conv":"1.2.2+pt24cu121"},"checkpoints":[]},"uma_env":{"status":"ready","built_at":"2026-03-16T15:59:22.197145+00:00","source_hash":"sha256:b8446f98c4c8ad7ff12f9bf39e5658bd45a989662cfa644bf48859bf7fd821c3","source":"# /// script\n# requires-python = \">=3.10,<3.11\"\n# dependencies = [\n#     \"torch>=2.4.0\",\n#     \"fairchem-core>=2.0.0\",\n#     \"ase>=3.22\",\n#     \"torch-geometric\",\n# ]\n#\n# [tool.uv]\n# find-links = [\"https://data.pyg.org/whl/torch-2.4.0+cu121.html\"]\n# ///\n\"\"\"\nUMA (Universal Atomistic Model) environment for Rootstock.\n\nThis environment provides access to Meta's UMA foundation model\nvia the FAIRChem library.\n\nModels:\n    - \"uma-s-1p1\": UMA small model (default)\n    - Other UMA variants as released by FAIRChem\n\"\"\"\n\n\ndef setup(model: str = \"uma-s-1p1\", device: str = \"cuda\"):\n    \"\"\"\n    Load a UMA calculator.\n\n    Args:\n        model: Model identifier (e.g., \"uma-s-1p1\"). Passed directly to\n               pretrained_mlip.get_predict_unit().\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from fairchem.core import FAIRChemCalculator, pretrained_mlip\n\n    predictor = pretrained_mlip.get_predict_unit(model, device=device)\n    return FAIRChemCalculator(predictor, task_name=\"omat\")\n","python_requires":">=3.10,<3.11","dependencies":{"ase":"3.27.0","fairchem-core":"2.14.0","rootstock":"0.6.1","torch":"2.8.0","torch-geometric":"2.7.0"},"checkpoints":[]},"mace_env":{"status":"ready","built_at":"2026-03-16T16:01:51.169812+00:00","source_hash":"sha256:8f49eb565411b7d3f015383c4e5d52ca9770d4295d9cb2c51c10cfa8ab173e75","source":"# /// script\n# requires-python = \">=3.10\"\n# dependencies = [\n#     \"mace-torch>=0.3.0\",\n#     \"ase>=3.22\",\n#     \"torch>=2.4.0,<2.10\",\n# ]\n# ///\n\"\"\"\nMACE environment for Rootstock.\n\nThis environment provides access to MACE foundation models for\nmachine learning interatomic potentials.\n\nModels:\n    - \"small\", \"medium\", \"large\": Pre-trained MACE-MP-0 models\n    - Path to a .pt file: Custom fine-tuned model\n\"\"\"\n\n\ndef setup(model: str, device: str = \"cuda\"):\n    \"\"\"\n    Load a MACE calculator.\n\n    Args:\n        model: Model identifier. Can be:\n            - \"small\", \"medium\", \"large\" for MACE-MP-0 foundation models\n            - Path to a .pt file for custom models\n        device: PyTorch device string (e.g., \"cuda\", \"cuda:0\", \"cpu\")\n\n    Returns:\n        ASE-compatible calculator\n    \"\"\"\n    from mace.calculators import mace_mp\n\n    return mace_mp(model=model, device=device, default_dtype=\"float32\")\n","python_requires":">=3.10","dependencies":{"ase":"3.27.0","mace-torch":"0.3.15","rootstock":"0.6.1","torch":"2.9.1"},"checkpoints":[]}}}]}