MDescriptor

MDescriptor is a batch-oriented periodic atomic-descriptor library. The numerical kernels are implemented in C++17 and all Python descriptors share one input/result/lifecycle contract.

MDescriptor 是面向批量周期结构的原子描述符库。数值核心使用 C++17；所有 Python 描述符共享统一的输入、结果和生命周期契约。

Layout / 布局

The public namespace is intentionally small:

src/mdescriptor/             installable package
src/mdescriptor/core/         Descriptor, StructureBatch, DescriptorResult
src/mdescriptor/descriptors/
  standalone/                 no model required (matrices/, many_body/, local/, rotational/)
  model_backed/               graph seam plus NEP, DPA4, DPA4C
src/mdescriptor/models/assets/ packaged, hash-verified model resources

standalone descriptors never require a model file. model_backed descriptors always resolve a model resource (a packaged default or an explicit model= path). There are no network downloads or implicit model discovery.

standalone 中的描述符不需要模型文件；model_backed 中的 NEP、DPA4、DPA4C 始终解析模型资源（内置默认模型或显式 model= 路径）。不会联网下载，也不会 扫描目录自动发现模型。

Installation / 安装

The base package requires Python 3.10+, NumPy and array-api-compat. ASE is optional and is only needed for StructureBatch.from_ase or direct ASE input. Sparse output is provided as SciPy CSR by the optional sparse extra.

基础包需要 Python 3.10+、NumPy 和 array-api-compat。ASE 只在使用 StructureBatch.from_ase 或直接传入 ASE 对象时需要；稀疏输出由可选的 sparse extra 提供。

python -m pip install .
python -m pip install ".[ase]"
python -m pip install ".[sparse]"

DPA4 and DPA4C are CPU-only pure-NumPy implementations. Their official .pt checkpoints are parsed by the bundled restricted reader without importing or installing Torch. No network model download is performed.

Input contract / 输入契约

import numpy as np
from mdescriptor import StructureBatch

batch = StructureBatch(
    numbers=np.array([1, 8], dtype=np.int32),
    positions=np.array([[0., 0., 0.], [1., 0., 0.]]),
    cells=np.eye(3, dtype=np.float64)[None] * 12,
    pbc=np.ones((1, 3), dtype=np.int32),
    offsets=np.array([0, 2], dtype=np.int64),
    ids=("water-0",),
)

StructureBatch validates contiguous arrays, finite positions/cells, nonsingular cells, positive atomic numbers, monotonic offsets and fully periodic pbc == (1, 1, 1). ASE conversion is available through StructureBatch.from_ase(...).

Descriptor API / 描述符 API

Algorithm classes live under mdescriptor.descriptors and use canonical names:

from mdescriptor.descriptors import SOAP

soap = SOAP(species=[1, 8], r_cut=4.5, n_max=4, l_max=3, average="off")
result = soap.compute(batch)

assert result.level == "atom"
assert result.values.shape == (batch.atoms, soap.feature_count)
assert len(result.labels) == result.values.shape[1]

with SOAP(species=[1, 8], r_cut=4.5, n_max=2, l_max=2) as descriptor:
    result = descriptor.compute(batch)

Every descriptor has idempotent close(), a closed property and synchronous context-manager support. Computing after close raises ClosedDescriptorError. Instances are synchronous and not promised to be thread-safe.

每个描述符都提供幂等 close()、closed 属性和同步上下文管理器；关闭后计算 会抛出 ClosedDescriptorError。实例是同步的，不承诺线程安全。

DescriptorResult contains values, level (atom, structure, or pair), structure_ids, row offsets, stable labels, JSON-safe metadata, per-row samples, and feature_count. The descriptor itself also retains the latest JSON-safe metadata and its configuration after close(). The standard shapes are (N, F), (S, F), and (P, F) for atom, structure and pair outputs respectively.

Registry / 注册表

Built-ins come from one explicit immutable specification list. Imports are lazy and no decorator or filesystem scan is used.

import mdescriptor
from mdescriptor.descriptors import SOAP

print(mdescriptor.list_descriptors())
soap = SOAP(species=[1, 8], r_cut=4.5, n_max=2, l_max=2)
rebuilt = mdescriptor.create_descriptor(soap.configuration)

child = mdescriptor.DescriptorRegistry(parent=mdescriptor.builtin_registry)
child.register(my_spec)

The built-in list separates AssetPolicy.NONE, OPTIONAL (for example MTP potentials), and REQUIRED (NEP/DPA4/DPA4C). The root package exposes stable contracts, registry functions and errors only; algorithm implementations are not re-exported from the root.

Model resources / 模型资源

ModelResource, ModelResolver, LoadedModel and ModelSession are the shared model-resource seam. Resolution is explicit, local and checksum-aware:

from pathlib import Path
from mdescriptor.descriptors import DPA4
from mdescriptor import ExecutionOptions

dpa4 = DPA4(
    model=Path("/path/to/official-checkpoint.pt"),
    execution=ExecutionOptions(device="cpu"),
)
result = dpa4.compute(batch)

The DPA4/DPA4C neural-network internals remain vendor/black-box adapters in this phase; they are intentionally isolated for a later dedicated DPA-series refactor.

Development / 开发

.venv/bin/python -m pip install -e . --no-build-isolation
.venv/bin/python -m pytest --import-mode=importlib tests -q

The extension is private (mdescriptor._native). C++ shared math and batch helpers live in named headers under cpp/include/mdescriptor/detail/.

MDescriptor is licensed under the GNU General Public License v3.0; see LICENSE.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.3.3)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for nicheal_code from gravatar.com nicheal_code

License

GNU GENERAL PUBLIC LICENSE

Requires

Python >=3.10

Provides Extra

ase sparse test reference all dev

Report project as malware

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Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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0.3.3

16 files

0.3.2

16 files

0.3.1

16 files

0.3.0

16 files

0.2.8

16 files

0.2.7

16 files

0.2.6

16 files

0.2.5

16 files

0.2.4

16 files

0.2.3

16 files

0.2.2

16 files

This release

0.2.1 This release

16 files

0.1.0

21 files

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