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ELFES

ELFES (/ɛlvz/) is a unified Equivariant Learning Framework for Electronic Structure.

Build status

Install from source

ELFES requires Python 3.12, 3.13, or 3.14 and a C++ compiler with C++20 support.

From the repository root, install ELFES into a supported Python environment:

pip install .

The default build compiles ELFES's NumPy CPU, PyTorch CPU, and PyTorch CUDA native providers. It requires a CUDA-enabled PyTorch installation, a compatible NVIDIA driver, and a local CUDA toolkit containing nvcc; the toolkit and PyTorch CUDA runtime must have the same major version.

For a CPU-only installation, disable the CUDA providers explicitly:

BUILD_CUDA=0 pip install .

Developers can use uv to install the exact locked development environment:

uv sync --locked

See Development for running commands in that environment, testing, formatting, type checking, and external test fixtures.

Run a workflow

Repository-level workflows are executable research programs that compose ELFES's Python APIs for a specific task. From the repository root, a typical workflow is run as:

uv run python workflows/workflow_name.py [arguments]

Each workflow defines its own inputs, parameters, and outputs; consult the script for its actual invocation.

License

ELFES is distributed under the BSD 3-Clause License.

Key dates

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Latest release

1 maintainer

PyPI data
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Avatar for ftsong from gravatar.com ftsong

Credits

Author: Feitong Song

License expression

BSD-3-Clause
View SPDX License List

Requires

Python <3.15, >=3.12

Classifiers

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