symfc
This is the symfc module.
Decision gist · record as of 2026-08-14
Yes, if you work with phonon calculations and need to enforce symmetry on computed force constants. The package is actively maintained, has low install friction, and no known security issues. License status is unclear, so verify licensing terms before use in proprietary work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later
- Low friction: pure Python wheel with three standard scientific dependencies (numpy, scipy, spglib).
- Repository is active with recent commits and no archived status.
License · maintenance · safety
(unclear)
last release 2026-06-29 (46 days) · last repo commit 2026-08-12 · 29 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 210,154 downloads/mo, #9,502 on PyPI
Alternatives
Verify before relying
pip install symfc
import symfc
# Load displacement-force dataset and compute symmetry-constrained force constants- Whether symfc is suitable for production phonon workflows or primarily a research tool
- Performance characteristics on large supercells or high-dimensional datasets
- Availability and completeness of documentation beyond the GitHub repository
What it is and what it does
Symfc is a Python package for computing force constants from supercell phonon calculations. It takes datasets of atomic forces and displacements from supercell snapshots and derives force constants while enforcing the symmetry constraints that these quantities must obey in crystals. In the phonon model, force constants are the Taylor-series expansion coefficients of the crystal potential with respect to atomic displacements; they determine phonon frequencies and other vibrational properties. Raw calculations often violate these symmetries due to numerical noise or approximation errors, and symfc corrects this by projecting the computed constants onto the symmetry-allowed subspace.
The package depends on numpy, scipy, and spglib for array operations, numerical methods, and crystal symmetry detection. It requires Python 3.10 or later and installs as a pure wheel with low friction. The repository is actively maintained.
Use it for
- Refine force constants from first-principles phonon calculations to satisfy crystal symmetry requirements
- Post-process supercell displacement-force datasets to remove numerical noise while preserving physical constraints
- Prepare symmetry-correct force constants for downstream phonon band structure and thermal property calculations
- Validate or improve force constant matrices obtained from machine learning or empirical potentials
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with phonon calculations and need to enforce symmetry on computed force constants.
The package is actively maintained, has low install friction, and no known security issues. License status is unclear, so verify licensing terms before use in proprietary work.
Install
symfc on PyPI
Before you install
Low friction: pure Python wheel with three standard scientific dependencies (numpy, scipy, spglib). Repository is active with recent commits and no archived status.
Requires Python 3.10 or later
Quickstart
pip install symfc
import symfc
# Load displacement-force dataset and compute symmetry-constrained force constantsVerify before relying
- Whether symfc is suitable for production phonon workflows or primarily a research tool
- Performance characteristics on large supercells or high-dimensional datasets
- Availability and completeness of documentation beyond the GitHub repository
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpyscipyspglib |
| Maintenance | Actively maintained 46 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 210,154 / month, #9,502 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: symfc-1.7.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “force constants crystal symmetry”
- symfcSymfc computes force constants from atomic displacement-force…
- phonopyPhonopy calculates phonon properties and vibrational spectra for…
- spglibspglib provides Python bindings to a C library for identifying and…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also phonopy · pyxtal · seekpath · chgnet · spglib · moyopy · pyocse · periodictable · mace-torch · gemmi