first release

LICENSE

@@ -0,0 +1,201 @@
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README.md

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+<div align="center">
+ 👋 Hi, everyone! 
+    <br>
+    We are <b>ByteDance Seed team.</b>
+</div>
+
+<p align="center">
+  You can get to know us better through the following channels👇
+  <br>
+  <a href="https://seed.bytedance.com/">
+    <img src="https://img.shields.io/badge/Website-%231e37ff?style=for-the-badge&logo=bytedance&logoColor=white"></a>
+  <a href="https://github.com/user-attachments/assets/5793e67c-79bb-4a59-811a-fcc7ed510bd4">
+    <img src="https://img.shields.io/badge/WeChat-07C160?style=for-the-badge&logo=wechat&logoColor=white"></a>
+ <a href="https://www.xiaohongshu.com/user/profile/668e7e15000000000303157d?xsec_token=ABl2-aqekpytY6A8TuxjrwnZskU-6BsMRE_ufQQaSAvjc%3D&xsec_source=pc_search">
+    <img src="https://img.shields.io/badge/Xiaohongshu-%23FF2442?style=for-the-badge&logo=xiaohongshu&logoColor=white"></a>
+  <a href="https://www.zhihu.com/org/dou-bao-da-mo-xing-tuan-dui/">
+    <img src="https://img.shields.io/badge/zhihu-%230084FF?style=for-the-badge&logo=zhihu&logoColor=white"></a>
+</p>
+
+![seed logo](https://github.com/user-attachments/assets/c42e675e-497c-4508-8bb9-093ad4d1f216)
+
+
+# Towards A Universally Transferable Acceleration Method for Density Functional Theory
+Zhe Liu, Yuyan Ni, Zhichen Pu, Qiming Sun, Siyuan Liu & Wen Yan
+
+https://arxiv.org/abs/2509.25724
+
+# TL;DR
+
+We propose a framework for accelerating DFT calculations.
+
+We train E(3)-equivariant neural networks to predict the expansion coefficients of the electron density in an auxiliary basis, and use the prediction to construct an initial guess for the SCF process. This approach exhibits superior transferability in various aspects.
+
+# Contents
+
+The repo currently contains the following contents:
+
+* The full SCFbench dataset.
+* The data pipeline for the SCFbench dataset.
+* The PyTorch `nn.Module` of the species-wise linear layer for the prediction of the electron density coefficients.
+* The NequIP model architecture with the species-wise linear layer.
+* Example code for computing the density coefficients from a density matrix.
+
+We will also release the following items soon:
+
+* The training code for models.
+* The full evaluation code.
+
+
+# Requirements
+
+* torch
+* e3nn
+* pyscf
+* lmdb
+* numpy>1.26
+* nequip (if you want to use the NequIP model)
+
+
+# Dataset Usage
+
+The sample dataset contains the `main` dataset (the dataset for training, validation and in-distribution testing) and the `ood-test` dataset.
+
+Each dataset contains several `parts`, each of which corresponds to a specific piece of information. The parts are:
+
+* `base`: the basic information of the molecule, including atomic numbers, coordinates, etc.
+* `dm`: the density matrix of the molecule.
+* `fock`: the Hamiltonian (fock) matrix of the molecule.
+* `auxdensity.denfit`: the density coefficients on def2-universal-jfit.
+* `auxdensity.denfit.etb2.0`: the density coefficients on the ETB basis of def2-svp with $\beta=2.0$.
+* `auxdensity.denfit.etb1.5`: the density coefficients on the ETB basis of def2-svp with $\beta=1.5$.
+
+Example:
+
+```python
+from dataset import SCFBenchDataset
+
+# Loading base info (atomic numbers, coordinates, etc.), density matrix, Hamiltonian (fock) matrix and the density coefficients on def2-universal-jfit.
+parts_to_load = ['base', 'dm', 'fock', 'auxdensity.denfit']
+dataset = SCFBenchDataset(data_root='dataset/main', parts_to_load=parts_to_load)
+dataset[0].keys()
+
+# Loading the base info and the density coefficients on the ETB basis of def2-svp with $\beta=1.5$.
+parts_to_load = ['base', 'auxdensity.denfit.etb1.5']
+dataset = SCFBenchDataset(data_root='dataset/ood-test', parts_to_load=parts_to_load, auxbasis='etb:def2-svp:1.5') 
+dataset[0].keys()
+
+# for the raw data, use the underlying dataset
+dataset.dataset[0].keys()
+```
+
+# Citing SCFbench
+If you use SCFbench in your research, please cite:
+```latex
+@misc{liu2025universallytransferableaccelerationmethod,
+      title={Towards A Universally Transferable Acceleration Method for Density Functional Theory}, 
+      author={Zhe Liu and Yuyan Ni and Zhichen Pu and Qiming Sun and Siyuan Liu and Wen Yan},
+      year={2025},
+      eprint={2509.25724},
+      archivePrefix={arXiv},
+      primaryClass={physics.chem-ph},
+      url={https://arxiv.org/abs/2509.25724}, 
+}
+```
+
+
+## License
+
+Models are licensed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).
+
+The dataset is a derivative of [ChEMBL](https://www.ebi.ac.uk/chembl/), used under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). 
+
+Our modified version, the SCFbench dataset, is also licensed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
+
+## About [ByteDance Seed Team](https://seed.bytedance.com/)
+
+Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.

dataset.py

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+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+# SPDX-License-Identifier: Apache-2.0 
+
+import os
+import lmdb
+import pickle
+
+from functools import lru_cache
+
+import numpy as np
+
+import torch
+from torch.utils.data import Dataset
+
+from gto import (
+    element_to_atomic_number,
+    GTOBasis,
+    GTOAuxDensityHelper,
+    GTOProductBasisHelper,
+)
+
+
+def compute_edge_index(coords, r_max, remove_self_loops=True):
+    from scipy.spatial import distance_matrix
+
+    dist = distance_matrix(coords, coords)
+    edge_index = np.stack(np.nonzero(dist < r_max), axis=0)
+    if remove_self_loops:
+        edge_index = edge_index[:, edge_index[0] != edge_index[1]]
+    return edge_index
+
+
+class ShardedLMDBDataset(Dataset):
+    def __init__(self, data_root: str):
+        super().__init__()
+        self.data_root = data_root
+        if os.path.isfile(os.path.join(data_root, "data.lmdb")):
+            self.shards = ["."]
+        else:
+            self.shards = sorted(os.listdir(data_root))
+        envs = self.get_envs()
+        self.env_lengths = [env.stat()["entries"] for env in envs]
+        self.env_boundaries = np.cumsum(self.env_lengths)
+        self.len = self.env_boundaries[-1]
+
+        self.envs = None  # postpone env intitialization until ddp is intitialized
+
+    def get_envs(self):
+        return [
+            lmdb.Environment(
+                os.path.join(self.data_root, shard, "data.lmdb"),
+                map_size=(1024**3) * 256,
+                subdir=False,
+                readonly=True,
+                readahead=True,
+                meminit=False,
+                lock=False,
+            )
+            for shard in self.shards
+        ]
+
+    def __len__(self):
+        return self.len
+
+    def __getitem__(self, index: int):
+        if self.envs is None:
+            self.envs = self.get_envs()
+        if index < 0 or index >= self.len:
+            raise IndexError
+        env_idx = np.searchsorted(self.env_boundaries, index, "right")
+        data_idx = index - (self.env_boundaries[env_idx - 1] if env_idx != 0 else 0)
+        x = pickle.loads(
+            self.envs[env_idx].begin(write=False).get(f"{data_idx}".encode())
+        )
+        return x
+
+
+class MultipartLMDBDataset(Dataset):
+    def __init__(self, data_root: str, parts_to_load: list[str] = ["base"]):
+        super().__init__()
+        self.data_root = data_root
+        self.subdatasets = {
+            part: ShardedLMDBDataset(os.path.join(data_root, part))
+            for part in parts_to_load
+        }
+        self.len = len(next(iter(self.subdatasets.values())))
+        assert all(
+            len(subdataset) == self.len for subdataset in self.subdatasets.values()
+        )
+
+    def __len__(self):
+        return self.len
+
+    def __getitem__(self, index: int):
+        ret = {}
+        for part, subdataset in self.subdatasets.items():
+            ret.update(subdataset[index])
+        return ret
+
+
+class SCFBenchDataset(Dataset):
+    """
+    Unit assumption:
+        atomic coordinates: angstrom
+        multipole moments: atomic unit
+        auxdensity: atomic unit
+        dm: atomic unit
+        fock: atomic unit
+    """
+
+    def __init__(
+        self,
+        data_root,
+        r_max=5.0,
+        type_names=["H", "C", "N", "O", "F", "P", "S"],
+        remove_self_loops=True,
+        parts_to_load=["base", "dm", "fock", "auxdensity.denfit"],
+        aobasis="def2-svp",
+        auxbasis="def2-universal-jfit",
+        use_denfit_ovlp=False,
+    ):
+        super().__init__()
+
+        self.data_root = data_root
+        self.parts_to_load = parts_to_load
+
+        self.dataset = MultipartLMDBDataset(
+            self.data_root, parts_to_load=self.parts_to_load
+        )
+
+        self.type_names = type_names
+        self.atom_numbers = [element_to_atomic_number[e] for e in self.type_names]
+        self.atom_number_to_index = {z: i for i, z in enumerate(self.atom_numbers)}
+
+        self.data_r_max = r_max
+        self.remove_self_loops = remove_self_loops
+
+        assert sum(["auxdensity" in p for p in parts_to_load]) <= 1, (
+            "Only one kind of auxdensity can be loaded."
+        )
+
+        if any(p.startswith("auxdensity") for p in parts_to_load):
+            self.auxbasis = GTOBasis.from_basis_name(auxbasis, elements=type_names)
+            self.use_denfit_ovlp = use_denfit_ovlp
+
+        if "dm" in parts_to_load or "fock" in parts_to_load or "mo" in parts_to_load:
+            self.aobasis = GTOBasis.from_basis_name(aobasis, elements=type_names)
+            self.ao_prod_basis = GTOProductBasisHelper(self.aobasis)
+
+    def __len__(self):
+        return len(self.dataset)
+
+    @lru_cache(maxsize=16)
+    def __getitem__(self, idx):
+        d = self.dataset[idx].copy()
+
+        d["atom_coords"] = d["atom_coords"]
+
+        d["edge_index"] = compute_edge_index(
+            d["atom_coords"], self.data_r_max, self.remove_self_loops
+        )
+
+        ret = {
+            "z": torch.LongTensor(
+                [self.atom_number_to_index[n] for n in d["atom_number"]]
+            ),
+            "pos": torch.FloatTensor(d["atom_coords"]),
+            "net_charge": torch.LongTensor([int(d["net_charge"])]),
+            "spin": torch.LongTensor([int(d["spin"])]),
+            "edge_index": torch.LongTensor(d["edge_index"]),
+        }
+
+        if any(p.startswith("auxdensity") for p in self.parts_to_load):
+            if self.use_denfit_ovlp:
+                auxdensity_key = "aux_density_denfit_ovlp"
+            else:
+                auxdensity_key = (
+                    "aux_density_jfit"
+                    if "auxdensity.jfit" in self.parts_to_load
+                    else "aux_density_denfit"
+                )
+            gtoaux = GTOAuxDensityHelper(d["atom_number"], self.auxbasis)
+            auxdensity_by_element = gtoaux.split_ao_by_elements(
+                gtoaux.transform_from_pyscf_to_std(d[auxdensity_key])
+            )
+            ret.update(
+                {
+                    "auxdensity": {
+                        k: torch.FloatTensor(t)
+                        for k, t in auxdensity_by_element.items()
+                    },
+                    "species_indices": {
+                        k: torch.IntTensor(t)
+                        for k, t in gtoaux.atom_indices_by_element.items()
+                    },
+                }
+            )
+
+        if "dm" in self.parts_to_load:
+            (
+                dm_diag_blocks,
+                dm_diag_masks,
+                dm_tril_blocks,
+                dm_tril_masks,
+                dm_tril_edge_index,
+            ) = self.ao_prod_basis.split_matrix_to_padded_blocks(
+                d["atom_number"],
+                self.ao_prod_basis.transform_from_pyscf_to_std(
+                    d["atom_number"], d["density_matrix"]
+                ),
+            )
+            ret.update(
+                {
+                    "dm_diag_blocks": torch.FloatTensor(dm_diag_blocks),
+                    "dm_diag_masks": torch.BoolTensor(dm_diag_masks),
+                    "dm_tril_blocks": torch.FloatTensor(dm_tril_blocks),
+                    "dm_tril_masks": torch.BoolTensor(dm_tril_masks),
+                    "dm_tril_edge_index": torch.IntTensor(dm_tril_edge_index),
+                }
+            )
+
+        if "fock" in self.parts_to_load:
+            (
+                fock_diag_blocks,
+                fock_diag_masks,
+                fock_tril_blocks,
+                fock_tril_masks,
+                fock_tril_edge_index,
+            ) = self.ao_prod_basis.split_matrix_to_padded_blocks(
+                d["atom_number"],
+                self.ao_prod_basis.transform_from_pyscf_to_std(
+                    d["atom_number"], d["fock"]
+                ),
+            )
+            ret.update(
+                {
+                    "fock_diag_blocks": torch.FloatTensor(fock_diag_blocks),
+                    "fock_diag_masks": torch.BoolTensor(fock_diag_masks),
+                    "fock_tril_blocks": torch.FloatTensor(fock_tril_blocks),
+                    "fock_tril_masks": torch.BoolTensor(fock_tril_masks),
+                    "fock_tril_edge_index": torch.IntTensor(fock_tril_edge_index),
+                }
+            )
+
+        return ret

dataset/LICENSE.txt

@@ -0,0 +1,359 @@
+Creative Commons Legal Code
+
+Attribution-ShareAlike 3.0 Unported
+
+    CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE
+    LEGAL SERVICES. DISTRIBUTION OF THIS LICENSE DOES NOT CREATE AN
+    ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS
+    INFORMATION ON AN "AS-IS" BASIS. CREATIVE COMMONS MAKES NO WARRANTIES
+    REGARDING THE INFORMATION PROVIDED, AND DISCLAIMS LIABILITY FOR
+    DAMAGES RESULTING FROM ITS USE.
+
+License
+
+THE WORK (AS DEFINED BELOW) IS PROVIDED UNDER THE TERMS OF THIS CREATIVE
+COMMONS PUBLIC LICENSE ("CCPL" OR "LICENSE"). THE WORK IS PROTECTED BY
+COPYRIGHT AND/OR OTHER APPLICABLE LAW. ANY USE OF THE WORK OTHER THAN AS
+AUTHORIZED UNDER THIS LICENSE OR COPYRIGHT LAW IS PROHIBITED.
+
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dataset/README.md

@@ -0,0 +1,51 @@
+<div align="center">
+ 👋 Hi, everyone! 
+    <br>
+    We are <b>ByteDance Seed team.</b>
+</div>
+
+<p align="center">
+  You can get to know us better through the following channels👇
+  <br>
+  <a href="https://seed.bytedance.com/">
+    <img src="https://img.shields.io/badge/Website-%231e37ff?style=for-the-badge&logo=bytedance&logoColor=white"></a>
+  <a href="https://github.com/user-attachments/assets/5793e67c-79bb-4a59-811a-fcc7ed510bd4">
+    <img src="https://img.shields.io/badge/WeChat-07C160?style=for-the-badge&logo=wechat&logoColor=white"></a>
+ <a href="https://www.xiaohongshu.com/user/profile/668e7e15000000000303157d?xsec_token=ABl2-aqekpytY6A8TuxjrwnZskU-6BsMRE_ufQQaSAvjc%3D&xsec_source=pc_search">
+    <img src="https://img.shields.io/badge/Xiaohongshu-%23FF2442?style=for-the-badge&logo=xiaohongshu&logoColor=white"></a>
+  <a href="https://www.zhihu.com/org/dou-bao-da-mo-xing-tuan-dui/">
+    <img src="https://img.shields.io/badge/zhihu-%230084FF?style=for-the-badge&logo=zhihu&logoColor=white"></a>
+</p>
+
+![seed logo](https://github.com/user-attachments/assets/c42e675e-497c-4508-8bb9-093ad4d1f216)
+
+
+# Towards A Universally Transferable Acceleration Method for Density Functional Theory
+Zhe Liu, Yuyan Ni, Zhichen Pu, Qiming Sun, Siyuan Liu & Wen Yan
+
+https://arxiv.org/abs/2509.25724
+
+# Citing SCFBench
+If you use SCFBench in your research, please cite:
+```latex
+@misc{liu2025universallytransferableaccelerationmethod,
+      title={Towards A Universally Transferable Acceleration Method for Density Functional Theory}, 
+      author={Zhe Liu and Yuyan Ni and Zhichen Pu and Qiming Sun and Siyuan Liu and Wen Yan},
+      year={2025},
+      eprint={2509.25724},
+      archivePrefix={arXiv},
+      primaryClass={physics.chem-ph},
+      url={https://arxiv.org/abs/2509.25724}, 
+}
+```
+
+
+## License
+
+The dataset is a derivative of [ChEMBL](https://www.ebi.ac.uk/chembl/), used under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/). 
+
+Our modified version, the SCFBench dataset, is also licensed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
+
+## About [ByteDance Seed Team](https://seed.bytedance.com/)
+
+Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.

dataset/main/auxdensity.denfit.etb1.5/data.lmdb

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+size 1733136384

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dataset/main/fock/data.lmdb

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dataset/ood-test/auxdensity.denfit.etb1.5/data.lmdb

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dataset/ood-test/auxdensity.denfit.etb2.0/data.lmdb

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dataset/ood-test/base/data.lmdb

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dataset/ood-test/fock/data.lmdb

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dm_to_density_example.py

@@ -0,0 +1,46 @@
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+# SPDX-License-Identifier: Apache-2.0 
+
+import numpy as np
+import pyscf
+import pyscf.df
+
+from dataset import MultipartLMDBDataset
+
+BASIS = "def2-svp"
+AUXBASIS = "def2-universal-jfit"
+
+
+def get_rho_auxbasis_denfit(features):
+    nums = features["atom_number"]
+    coords = features["atom_coords"]
+    charge = int(features["net_charge"])
+    spin = int(features["spin"])
+    dm = features["density_matrix"]
+
+    mol = pyscf.M(
+        atom=list(zip(nums.tolist(), coords.tolist())),
+        unit="angstrom",
+        basis=BASIS,
+        charge=charge,
+        spin=spin,
+    )
+    auxmol = pyscf.df.addons.make_auxmol(mol, auxbasis=AUXBASIS)
+
+    ints_3c1e = pyscf.df.incore.aux_e2(mol, auxmol, intor="int3c1e")
+    aux_vec = np.linalg.solve(
+        auxmol.intor("int1e_ovlp"), np.einsum("ij,ijp->p", dm, ints_3c1e)
+    )
+
+    return {"aux_density_denfit": aux_vec}
+
+
+def main():
+    dataset = MultipartLMDBDataset("dataset/main", parts_to_load=["base", "dm"])
+    d = dataset[0]
+    density_coeffs_dict = get_rho_auxbasis_denfit(d)
+    print(density_coeffs_dict["aux_density_denfit"])
+
+
+if __name__ == "__main__":
+    main()

gto.py

@@ -0,0 +1,411 @@
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+# SPDX-License-Identifier: Apache-2.0 
+
+from functools import lru_cache
+
+import numpy as np
+import pyscf
+from pyscf.data.elements import (
+    NUC as element_to_atomic_number,
+    ELEMENTS as atomic_number_to_element,
+)
+
+import torch
+from e3nn import o3
+
+
+def build_irreps_from_mol(mol):
+    parity = lambda l: "e" if l % 2 == 0 else "o"
+    irreps = o3.Irreps(
+        "+".join(f"{l}{parity(l)}" for l in map(mol.bas_angular, range(mol.nbas)))
+    )
+    return irreps
+
+
+def ref_etb(aobasis, beta, atom_type_names):
+    charges = [element_to_atomic_number[element] for element in atom_type_names]
+    spin = sum(charges) % 2
+    coords = np.zeros((len(charges), 3))
+    reference_mol = pyscf.M(atom=list(zip(charges, coords)), basis=aobasis, spin=spin)
+    basis = pyscf.df.aug_etb(reference_mol, beta)
+    return basis
+
+
+def parse_basis_name(basis_name, atom_type_names):
+    if basis_name.startswith("etb:"):
+        # example: etb:def2-svp:1.5
+        _, aobasis, beta = basis_name.split(":")
+        return ref_etb(aobasis, float(beta), atom_type_names)
+    else:
+        return basis_name
+
+
+def per_element_irreps_from_basis_name(basis_name, elements):
+    basis = parse_basis_name(basis_name, elements)
+    per_element_irreps = {}
+    if isinstance(basis, str):
+        for e in elements:
+            z = element_to_atomic_number[e]
+            if z % 2 != 0:
+                spin = 1
+            else:
+                spin = 0
+            mol = pyscf.M(atom=[[z, [0, 0, 0]]], basis=basis_name, charge=0, spin=spin)
+            per_element_irreps[e] = build_irreps_from_mol(mol)
+        return per_element_irreps
+    else:
+        parity = lambda l: "e" if l % 2 == 0 else "o"
+        irrep_from_item = lambda item: f"1x{item[0]}{parity(item[0])}"
+        for e in elements:
+            per_element_irreps[e] = o3.Irreps(
+                "+".join(irrep_from_item(i) for i in basis[e])
+            )
+        return per_element_irreps
+
+
+@lru_cache(maxsize=16)
+def pyscf_to_standard_perm_D_for_single_irrep(l, p, dtype=torch.float32):
+    if l == 1:
+        return torch.tensor([[0, 1, 0], [0, 0, 1], [1, 0, 0]], dtype=dtype)
+    else:
+        return torch.eye(2 * l + 1, dtype=dtype)
+
+
+@lru_cache(maxsize=32)
+def e3nn_change_of_coord_D_for_single_irrep(l, p, dtype=torch.float32):
+    cod = torch.tensor(
+        [
+            # this specifies the change of basis yzx -> xyz
+            [0.0, 0.0, 1.0],
+            [1.0, 0.0, 0.0],
+            [0.0, 1.0, 0.0],
+        ],
+        dtype=dtype,
+    )
+    irreps = o3.Irreps([(1, (l, p))])
+    return irreps.D_from_matrix(cod)
+
+
+@lru_cache(maxsize=32)
+def pyscf_to_e3nn_D_for_single_irrep(l, p, dtype=torch.float32):
+    pyscf_to_std = pyscf_to_standard_perm_D_for_single_irrep(l, p, dtype=dtype)
+    cod_D = e3nn_change_of_coord_D_for_single_irrep(l, p, dtype=dtype)
+    return pyscf_to_std @ cod_D
+
+
+def e3nn_change_of_coord_D(irreps, dtype=torch.float32):
+    perm = torch.block_diag(
+        *[
+            e3nn_change_of_coord_D_for_single_irrep(
+                orbital.ir.l, orbital.ir.p, dtype=dtype
+            )
+            for orbital in irreps
+        ]
+    )
+    return perm
+
+
+def pyscf_to_standard_perm_D(atomic_orbital_irreps, dtype=torch.float32):
+    perm = torch.block_diag(
+        *[
+            pyscf_to_standard_perm_D_for_single_irrep(
+                orbital.ir.l, orbital.ir.p, dtype=dtype
+            )
+            for orbital in atomic_orbital_irreps
+        ]
+    )
+    return perm.to(dtype=dtype)
+
+
+def get_pyscf_to_e3nn_D(atomic_orbital_irreps, dtype=torch.float32):
+    total_D = torch.block_diag(
+        *[
+            pyscf_to_e3nn_D_for_single_irrep(orbital.ir.l, orbital.ir.p, dtype=dtype)
+            for orbital in atomic_orbital_irreps
+        ]
+    )
+    return total_D
+
+
+def transform_from_pyscf_to_std_fast_1d(irreps, array: np.ndarray):
+    # only the l=1 orbitals need to be transformed: xyz -> yzx
+    assert array.shape[0] == irreps.dim
+    ret = array.copy()
+    start = 0
+    for orbital in irreps:
+        if orbital.ir.l == 1:
+            for _ in range(orbital.mul):
+                ret[start : start + 3] = np.roll(ret[start : start + 3], -1, axis=0)
+                start += 3
+        else:
+            start += orbital.dim
+    return ret
+
+
+def transform_from_std_to_pyscf_fast_1d(irreps, array: np.ndarray):
+    # only the l=1 orbitals need to be transformed: xyz -> yzx
+    assert array.shape[0] == irreps.dim
+    ret = array.copy()
+    start = 0
+    for orbital in irreps:
+        if orbital.ir.l == 1:
+            for _ in range(orbital.mul):
+                ret[start : start + 3] = np.roll(ret[start : start + 3], 1, axis=0)
+                start += 3
+        else:
+            start += orbital.dim
+    return ret
+
+
+def transform_from_pyscf_to_std_fast_2d(irreps, matrix: np.ndarray):
+    assert matrix.shape[0] == matrix.shape[1] == irreps.dim
+    ret = matrix.copy()
+    start = 0
+    for orbital in irreps:
+        if orbital.ir.l == 1:
+            for _ in range(orbital.mul):
+                ret[start : start + 3, :] = np.roll(
+                    ret[start : start + 3, :], -1, axis=0
+                )
+                ret[:, start : start + 3] = np.roll(
+                    ret[:, start : start + 3], -1, axis=1
+                )
+                start += 3
+        else:
+            start += orbital.dim
+    return ret
+
+
+def transform_from_std_to_pyscf_fast_2d(irreps, matrix: np.ndarray):
+    assert matrix.shape[0] == matrix.shape[1] == irreps.dim
+    ret = matrix.copy()
+    start = 0
+    for orbital in irreps:
+        if orbital.ir.l == 1:
+            for _ in range(orbital.mul):
+                ret[start : start + 3, :] = np.roll(
+                    ret[start : start + 3, :], 1, axis=0
+                )
+                ret[:, start : start + 3] = np.roll(
+                    ret[:, start : start + 3], 1, axis=1
+                )
+                start += 3
+        else:
+            start += orbital.dim
+    return ret
+
+
+class GTOBasis:
+    per_element_irreps: dict[str, o3.Irreps]
+    per_element_numel: dict[str, int]
+    allowed_elements: list[str]
+    allowed_atomic_numbers: list[int]
+
+    def __init__(self, per_element_irreps: dict[str, str]):
+        self.per_element_irreps = {}
+        self.per_element_numel = {}
+        self.allowed_elements = []
+        self.allowed_atomic_numbers = []
+
+        for elem, irstr in per_element_irreps.items():
+            irreps = o3.Irreps(irstr)
+            self.per_element_irreps[elem] = irreps
+            self.per_element_numel[elem] = irreps.dim
+            self.allowed_elements.append(elem)
+            self.allowed_atomic_numbers.append(element_to_atomic_number[elem])
+
+    @classmethod
+    def from_basis_name(cls, basis_name, elements: list[str]):
+        per_element_irreps = per_element_irreps_from_basis_name(basis_name, elements)
+        return cls(per_element_irreps=per_element_irreps)
+
+    def irreps_for_mol(self, atom_types: list[int]):
+        atom_elements = [atomic_number_to_element[z] for z in atom_types]
+        atomic_orbital_irreps = o3.Irreps(
+            "+".join(str(self.per_element_irreps[e]) for e in atom_elements)
+        )
+        return atomic_orbital_irreps
+
+
+class GTOProductBasisHelper:
+    basis: GTOBasis
+    padded_irrep: o3.Irreps
+    per_element_mask: dict[str, np.ndarray]
+
+    def __init__(self, basis: GTOBasis):
+        self.basis = basis
+
+        maxl = max(basis.per_element_irreps[e].lmax for e in basis.allowed_elements)
+        parity = lambda l: "e" if l % 2 == 0 else "o"
+        ls = list(range(maxl + 1))
+        l_irreps = [f"{l}{parity(l)}" for l in ls]
+
+        per_element_irrep_mul = {
+            e: list(basis.per_element_irreps[e].count(l_ir) for l_ir in l_irreps)
+            for e in basis.allowed_elements
+        }
+
+        max_mul = list(
+            max(per_element_irrep_mul[e][l] for e in basis.allowed_elements) for l in ls
+        )
+        max_irreps = "+".join(f"{mul}x{l_ir}" for mul, l_ir in zip(max_mul, l_irreps))
+        self.padded_irrep = o3.Irreps(max_irreps)
+
+        self.per_element_mask = {}
+        for e in self.basis.allowed_elements:
+            self.per_element_mask[e] = np.zeros(self.padded_irrep.dim, dtype=bool)
+            element_irreps = basis.per_element_irreps[e]
+            for l, l_ir, lslice in zip(ls, l_irreps, self.padded_irrep.slices()):
+                mul = element_irreps.count(l_ir)
+                l_dim = 2 * l + 1
+                valid_slice = slice(lslice.start, lslice.start + mul * l_dim)
+                self.per_element_mask[e][valid_slice] = True
+
+    def transform_from_pyscf_to_std(self, atom_types: list[int], matrix: np.ndarray):
+        # only the l=1 orbitals need to be transformed: xyz -> yzx
+        atomic_orbital_irreps = self.basis.irreps_for_mol(atom_types)
+        return transform_from_pyscf_to_std_fast_2d(atomic_orbital_irreps, matrix)
+
+    def transform_from_std_to_pyscf(self, atom_types: list[int], matrix: np.ndarray):
+        # only the l=1 orbitals need to be transformed: xyz -> yzx
+        atomic_orbital_irreps = self.basis.irreps_for_mol(atom_types)
+        return transform_from_std_to_pyscf_fast_2d(atomic_orbital_irreps, matrix)
+
+    def split_matrix_to_padded_blocks(
+        self,
+        atom_types: list[int],
+        matrix: np.ndarray,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        atom_types = np.array(atom_types, dtype=np.int32)
+        natom = len(atom_types)
+        atom_elements = [atomic_number_to_element[z] for z in atom_types]
+        atom_dims = [self.basis.per_element_numel[e] for e in atom_elements]
+        atom_starts = [0] + np.cumsum(atom_dims).tolist()[:-1]
+
+        diag_blocks = np.zeros(
+            (natom, self.padded_irrep.dim, self.padded_irrep.dim), dtype=matrix.dtype
+        )
+        diag_masks = np.zeros(
+            (natom, self.padded_irrep.dim, self.padded_irrep.dim), dtype=bool
+        )
+        tril_blocks = np.zeros(
+            (natom * (natom - 1) // 2, self.padded_irrep.dim, self.padded_irrep.dim),
+            dtype=matrix.dtype,
+        )
+        tril_masks = np.zeros(
+            (natom * (natom - 1) // 2, self.padded_irrep.dim, self.padded_irrep.dim),
+            dtype=bool,
+        )
+        tril_edge_index = np.zeros((2, (natom * (natom - 1) // 2)), dtype=np.int64)
+
+        for i in range(natom):
+            mask_i = self.per_element_mask[atom_elements[i]]
+            slice_i = slice(atom_starts[i], atom_starts[i] + atom_dims[i])
+            block = matrix[slice_i, slice_i]
+            diag_masks[i] = mask_i[:, None] & mask_i[None, :]
+            diag_blocks[i, diag_masks[i]] = block.reshape(-1)
+
+        iblock = 0
+        for i in range(0, natom):
+            mask_i = self.per_element_mask[atom_elements[i]]
+            slice_i = slice(atom_starts[i], atom_starts[i] + atom_dims[i])
+            for j in range(0, i):  # lower triangle
+                mask_j = self.per_element_mask[atom_elements[j]]
+                slice_j = slice(atom_starts[j], atom_starts[j] + atom_dims[j])
+                tril_masks[iblock, mask_i[:, None] & mask_j[None, :]] = True
+                tril_blocks[iblock, tril_masks[iblock]] = matrix[
+                    slice_i, slice_j
+                ].reshape(-1)
+                tril_edge_index[0, iblock] = i
+                tril_edge_index[1, iblock] = j
+                iblock += 1
+
+        return diag_blocks, diag_masks, tril_blocks, tril_masks, tril_edge_index
+
+    def assemble_matrix_from_padded_blocks(
+        self,
+        atom_types: list[int],
+        padded_diag_blocks: list[np.ndarray],
+        padded_tril_blocks: list[np.ndarray],  # in lower triangular order
+    ) -> np.ndarray:
+        atom_types = np.array(atom_types, dtype=np.int32)
+        natom = len(atom_types)
+        atom_elements = [atomic_number_to_element[z] for z in atom_types]
+        atom_dims = [self.basis.per_element_numel[e] for e in atom_elements]
+        atom_starts = [0] + np.cumsum(atom_dims).tolist()[:-1]
+
+        nao = sum(self.basis.per_element_numel[e] for e in atom_elements)
+        matrix = np.zeros((nao, nao), dtype=padded_diag_blocks[0].dtype)
+
+        for i in range(natom):
+            slice_i = slice(atom_starts[i], atom_starts[i] + atom_dims[i])
+            mask_i = self.per_element_mask[atom_elements[i]]
+            block = padded_diag_blocks[i][mask_i][:, mask_i]
+            matrix[slice_i, slice_i] = block
+
+        iblock = 0
+        for i in range(0, natom):
+            mask_i = self.per_element_mask[atom_elements[i]]
+            slice_i = slice(atom_starts[i], atom_starts[i] + atom_dims[i])
+            for j in range(0, i):  # lower triangle
+                mask_j = self.per_element_mask[atom_elements[j]]
+                slice_j = slice(atom_starts[j], atom_starts[j] + atom_dims[j])
+                block = padded_tril_blocks[iblock][mask_i][:, mask_j]
+                matrix[slice_i, slice_j] = block
+                matrix[slice_j, slice_i] = block.T
+                iblock += 1
+
+        return matrix
+
+
+class GTOAuxDensityHelper:
+    atom_types: list[int]
+    basis: GTOBasis
+    atomic_orbital_irreps: o3.Irreps
+    atomic_orbital_masks_by_element: dict[str, np.ndarray]
+    atom_indices_by_element: dict[str, np.ndarray]
+
+    def __init__(self, atom_types: list[int], basis: GTOBasis):
+        self.atom_types = np.array(atom_types, dtype=np.int32)
+        atom_elements = [atomic_number_to_element[z] for z in self.atom_types]
+        self.basis = basis
+        self.atomic_orbital_irreps = self.basis.irreps_for_mol(self.atom_types)
+
+        mask = np.concatenate(
+            [
+                [z] * basis.per_element_numel[e]
+                for e, z in zip(atom_elements, self.atom_types)
+            ]
+        )
+        self.atomic_orbital_masks_by_element = {
+            e: mask == z
+            for e, z in zip(basis.allowed_elements, basis.allowed_atomic_numbers)
+        }
+        self.atom_indices_by_element = {
+            e: np.nonzero(atom_types == z)[0]
+            for e, z in zip(basis.allowed_elements, basis.allowed_atomic_numbers)
+        }
+
+    def transform_from_pyscf_to_std(self, array: np.ndarray):
+        return transform_from_pyscf_to_std_fast_1d(self.atomic_orbital_irreps, array)
+
+    def transform_from_std_to_pyscf(self, array: np.ndarray):
+        return transform_from_std_to_pyscf_fast_1d(self.atomic_orbital_irreps, array)
+
+    def split_ao_by_elements(self, array: np.ndarray):
+        splitted = {
+            e: array[self.atomic_orbital_masks_by_element[e]].reshape(
+                -1, self.basis.per_element_numel[e]
+            )
+            for e in self.basis.allowed_elements
+        }
+        assert sum(len(v) for v in splitted.values()) == len(self.atom_types)
+        assert sum(v.size for v in splitted.values()) == self.atomic_orbital_irreps.dim
+        return splitted
+
+    def assemble_ao_from_per_element_arrays(self, splitted: dict[str, np.ndarray]):
+        dtype = next(iter(splitted.values())).dtype
+        array = np.zeros(self.atomic_orbital_irreps.dim, dtype=dtype)
+        for z, mask in self.atomic_orbital_masks_by_element.items():
+            array[mask] = splitted[z].reshape(-1)
+        return array

modules.py

@@ -0,0 +1,75 @@
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+# SPDX-License-Identifier: Apache-2.0 
+
+import torch
+import torch.nn as nn
+
+from e3nn import o3
+
+from nequip.data import AtomicDataDict
+from nequip.nn import GraphModuleMixin
+
+from gto import GTOBasis
+
+
+class AuxdensityHead(nn.Module):
+    def __init__(
+        self,
+        irreps_in: str | o3.Irreps,
+        type_names: list[str],
+        biases: bool = False,
+        auxbasis: str = "def2-universal-jfit",
+    ):
+        super().__init__()
+        self.auxbasis = GTOBasis.from_basis_name(auxbasis, elements=type_names)
+        self.species_list = list(self.auxbasis.per_element_irreps.keys())
+        self.per_species_modules = nn.ModuleDict(
+            {
+                species: o3.Linear(
+                    irreps_in=irreps_in, irreps_out=irreps_out, biases=biases
+                )
+                for species, irreps_out in self.auxbasis.per_element_irreps.items()
+            }
+        )
+
+    def forward(
+        self, atom_features: torch.Tensor, species_indices: dict[str, torch.Tensor]
+    ):
+        outputs = {}
+        for species in self.species_list:
+            outputs[species] = self.per_species_modules[species](
+                atom_features[species_indices[species]]
+            )
+        return outputs
+
+
+class AuxdensityHeadForNequip(GraphModuleMixin, torch.nn.Module):
+    def __init__(
+        self,
+        type_names: list[str],
+        auxbasis: str = "def2-universal-jfit",
+        field: str = AtomicDataDict.NODE_FEATURES_KEY,
+        out_field: str | None = None,
+        biases: bool = True,
+        irreps_in: str | o3.Irreps = None,
+    ):
+        super().__init__()
+        self.field = field
+        out_field = out_field if out_field is not None else field
+        self.out_field = out_field
+
+        self._init_irreps(
+            irreps_in=irreps_in,
+            required_irreps_in=[field],
+            # we do not init irreps_out here because this module should be the final layer
+        )
+        self.layer = AuxdensityHead(
+            irreps_in=irreps_in[field],
+            type_names=type_names,
+            auxbasis=auxbasis,
+            biases=biases,
+        )
+
+    def forward(self, data):
+        data[self.out_field] = self.layer(data[self.field], data["species_indices"])
+        return data

nequip_model.py

@@ -0,0 +1,284 @@
+# Copyright (c) 2021 The President and Fellows of Harvard College
+# Copyright (c) 2025 The NequIP Developers
+# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
+# SPDX-License-Identifier: MIT
+#
+# This file has been modified by ByteDance Ltd. and/or its affiliates on 2025-09-01.
+#
+# Original file was released under MIT, with the full license text
+# available at https://github.com/mir-group/nequip/blob/main/LICENSE.
+#
+# This modified file is released under the same license.
+
+import warnings
+from typing import Sequence, Optional, Callable, Any
+
+import math
+
+import torch
+import torch.nn as nn
+from e3nn import o3
+from e3nn.util.jit import compile_mode
+from nequip.nn import GraphModuleMixin
+from nequip.nn.utils import with_edge_vectors_
+
+from nequip.data import AtomicDataDict
+from nequip.nn import (
+    SequentialGraphNetwork,
+    ConvNetLayer,
+    ApplyFactor,
+)
+from nequip.nn.embedding import (
+    EdgeLengthNormalizer,
+    BesselEdgeLengthEncoding,
+    PolynomialCutoff,
+    NodeTypeEmbed,
+)
+
+from modules import AuxdensityHeadForNequip
+
+@compile_mode("script")
+class SphericalHarmonicEdgeAttrs(GraphModuleMixin, torch.nn.Module):
+    """Construct edge attrs as spherical harmonic projections of edge vectors.
+
+    Parameters follow ``e3nn.o3.spherical_harmonics``.
+
+    Args:
+        irreps_edge_sh (int, str, or o3.Irreps): if int, will be treated as lmax for o3.Irreps.spherical_harmonics(lmax)
+        edge_sh_normalization (str): the normalization scheme to use
+        edge_sh_normalize (bool, default: True): whether to normalize the spherical harmonics
+        out_field (str, default: AtomicDataDict.EDGE_ATTRS_KEY: data/irreps field
+    """
+
+    out_field: str
+
+    def __init__(
+        self,
+        irreps_edge_sh: int | str | o3.Irreps,
+        component_order: str = 'e3nn',
+        edge_sh_normalization: str = "component",
+        edge_sh_normalize: bool = True,
+        irreps_in=None,
+        out_field: str = AtomicDataDict.EDGE_ATTRS_KEY,
+    ):
+        super().__init__()
+        self.out_field = out_field
+
+        if isinstance(irreps_edge_sh, int):
+            self.irreps_edge_sh = o3.Irreps.spherical_harmonics(irreps_edge_sh)
+        else:
+            self.irreps_edge_sh = o3.Irreps(irreps_edge_sh)
+        self._init_irreps(
+            irreps_in=irreps_in,
+            irreps_out={out_field: self.irreps_edge_sh},
+        )
+        self.sh = o3.SphericalHarmonics(
+            self.irreps_edge_sh, edge_sh_normalize, edge_sh_normalization
+        )
+        # i.e. `model_dtype`
+        self._output_dtype = torch.get_default_dtype()
+
+        assert component_order in ['e3nn', 'std'], "component_order must be 'e3nn' or 'std'"
+        self.component_order = component_order
+
+    def forward(self, data: AtomicDataDict.Type) -> AtomicDataDict.Type:
+        data = with_edge_vectors_(data, with_lengths=False)
+        edge_vec = data[AtomicDataDict.EDGE_VECTORS_KEY]
+        if self.component_order == 'std':
+            edge_vec = edge_vec[:, [1, 2, 0]]
+        edge_sh = self.sh(edge_vec)
+        data[self.out_field] = edge_sh.to(self._output_dtype)
+        return data
+
+
+class NequipArch(nn.Module):
+    def __init__(
+        self,
+        r_max: float,
+        type_names: Sequence[str],
+        # convnet params
+        radial_mlp_depth: Sequence[int],
+        radial_mlp_width: Sequence[int],
+        feature_irreps_hidden: Sequence[str | o3.Irreps],
+        # irreps and dims
+        irreps_edge_sh: int | str | o3.Irreps,
+        type_embed_num_features: int,
+        # edge length encoding
+        per_edge_type_cutoff: Optional[dict[str, float | dict[str, float]]] = None,
+        num_bessels: int = 8,
+        bessel_trainable: bool = False,
+        polynomial_cutoff_p: int = 6,
+        # edge sum normalization
+        avg_num_neighbors: Optional[float] = None,
+        # == things that generally shouldn't be changed ==
+        # convnet
+        convnet_resnet: bool = False,
+        convnet_nonlinearity_type: str = "gate",
+        convnet_nonlinearity_scalars: dict[int, Callable] = {"e": "silu", "o": "tanh"},
+        convnet_nonlinearity_gates: dict[int, Callable] = {"e": "silu", "o": "tanh"},
+        task_head_specs: dict[str, Any] = {},
+        auxbasis: str = "def2-universal-jfit",
+    ):
+        super().__init__()
+
+        self.type_names = type_names
+
+        # === sanity checks and warnings ===
+        assert all(tn.isalnum() for tn in type_names), (
+            "`type_names` must contain only alphanumeric characters"
+        )
+
+        # require every convnet layer to be specified explicitly in a list
+        # infer num_layers from the list size
+        assert (
+            len(radial_mlp_depth) == len(radial_mlp_width) == len(feature_irreps_hidden)
+        ), (
+            f"radial_mlp_depth: {radial_mlp_depth}, radial_mlp_width: {radial_mlp_width}, feature_irreps_hidden: {feature_irreps_hidden} should all have the same length"
+        )
+        num_layers = len(radial_mlp_depth)
+
+        if avg_num_neighbors is None:
+            warnings.warn(
+                "Found `avg_num_neighbors=None` -- it is recommended to set `avg_num_neighbors` for normalization and better numerics during training."
+            )
+
+        # === encode and embed features ===
+        # == edge tensor embedding ==
+        spharm = SphericalHarmonicEdgeAttrs(
+            irreps_edge_sh=irreps_edge_sh,
+            component_order="std",
+        )
+        # == edge scalar embedding ==
+        edge_norm = EdgeLengthNormalizer(
+            r_max=r_max,
+            type_names=type_names,
+            per_edge_type_cutoff=per_edge_type_cutoff,
+            irreps_in=spharm.irreps_out,
+        )
+        bessel_encode = BesselEdgeLengthEncoding(
+            num_bessels=num_bessels,
+            trainable=bessel_trainable,
+            cutoff=PolynomialCutoff(polynomial_cutoff_p),
+            edge_invariant_field=AtomicDataDict.EDGE_EMBEDDING_KEY,
+            irreps_in=edge_norm.irreps_out,
+        )
+        # for backwards compatibility of NequIP's bessel encoding
+        factor = ApplyFactor(
+            in_field=AtomicDataDict.EDGE_EMBEDDING_KEY,
+            factor=(2 * math.pi) / (r_max * r_max),
+            irreps_in=bessel_encode.irreps_out,
+        )
+        # == node scalar embedding ==
+        type_embed = NodeTypeEmbed(
+            type_names=type_names,
+            num_features=type_embed_num_features,
+            irreps_in=factor.irreps_out,
+        )
+        modules = {
+            "spharm": spharm,
+            "edge_norm": edge_norm,
+            "bessel_encode": bessel_encode,
+            "factor": factor,
+            "type_embed": type_embed,
+        }
+        prev_irreps_out = type_embed.irreps_out
+
+        # === convnet layers ===
+        for layer_i in range(num_layers):
+            current_convnet = ConvNetLayer(
+                irreps_in=prev_irreps_out,
+                feature_irreps_hidden=feature_irreps_hidden[layer_i],
+                convolution_kwargs={
+                    "radial_mlp_depth": radial_mlp_depth[layer_i],
+                    "radial_mlp_width": radial_mlp_width[layer_i],
+                    "avg_num_neighbors": avg_num_neighbors,
+                    # to ensure isolated atom limit
+                    "use_sc": layer_i != 0,
+                },
+                resnet=(layer_i != 0) and convnet_resnet,
+                nonlinearity_type=convnet_nonlinearity_type,
+                nonlinearity_scalars=convnet_nonlinearity_scalars,
+                nonlinearity_gates=convnet_nonlinearity_gates,
+            )
+            prev_irreps_out = current_convnet.irreps_out
+            modules.update({f"layer{layer_i}_convnet": current_convnet})
+
+        # === assemble in SequentialGraphNetwork ===
+        self.backbone = SequentialGraphNetwork(modules)
+
+        # === readout ===
+        self.backbone_irreps_out = prev_irreps_out
+        self.task_head = SequentialGraphNetwork(
+            {
+                "auxdensity_atom_readout": AuxdensityHeadForNequip(
+                    type_names=self.type_names,
+                    auxbasis=auxbasis,
+                    field=AtomicDataDict.NODE_FEATURES_KEY,
+                    out_field="output:auxdensity",
+                    biases=True,
+                    irreps_in=self.backbone_irreps_out,
+                ),
+            }
+        )
+
+    def convert_inputs(self, inputs):
+        ret = inputs.copy()
+        ret.update(
+            {
+                AtomicDataDict.ATOM_TYPE_KEY: inputs["z"],
+                AtomicDataDict.POSITIONS_KEY: inputs["pos"],
+                AtomicDataDict.EDGE_INDEX_KEY: inputs["edge_index"],
+            }
+        )
+        return ret
+
+    def forward(self, data):
+        data = self.convert_inputs(data)
+        data = self.backbone(data)
+        data = self.task_head(data)
+        return data
+
+
+def nequip_simple_builder(
+    num_layers: int = 4,
+    l_max: int = 1,
+    parity: bool = True,
+    num_features: int = 32,
+    radial_mlp_depth: int = 2,
+    radial_mlp_width: int = 64,
+    **kwargs,
+) -> nn.Module:
+    irreps_edge_sh = repr(
+        o3.Irreps.spherical_harmonics(lmax=l_max, p=-1 if parity else 1)
+    )
+    feature_irreps_hidden = repr(
+        o3.Irreps(
+            [
+                (num_features, (l, p))
+                for p in ((1, -1) if parity else (1,))
+                for l in range(l_max + 1)
+            ]
+        )
+    )
+    feature_irreps_hidden_list = [feature_irreps_hidden] * (num_layers - 1)
+    radial_mlp_depth_list = [radial_mlp_depth] * num_layers
+    radial_mlp_width_list = [radial_mlp_width] * num_layers
+
+    feature_irreps_hidden_list += [feature_irreps_hidden]
+
+    model = NequipArch(
+        irreps_edge_sh=irreps_edge_sh,
+        type_embed_num_features=num_features,
+        feature_irreps_hidden=feature_irreps_hidden_list,
+        radial_mlp_depth=radial_mlp_depth_list,
+        radial_mlp_width=radial_mlp_width_list,
+        **kwargs,
+    )
+    return model
+
+
+if __name__ == "__main__":
+    model = nequip_simple_builder(r_max=5.0, type_names=["H", "C", "N", "O", "F", "P", "S"])
+    from dataset import SCFBenchDataset
+    dataset = SCFBenchDataset("dataset/main", parts_to_load=["base", "auxdensity.denfit"])
+    print(model(dataset[0])['output:auxdensity'])

requirements.txt

@@ -0,0 +1,5 @@
+numpy>1.16,<2.0
+lmdb
+torch
+e3nn
+nequip