Semi-empirical potential fitting in Python

Navigation

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for prnvrvs from gravatar.com prnvrvs

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License Expression: MIT
    SPDX License Expression
  • Author: Pranav Kumar
  • Requires: Python >=3.11
  • Provides-Extra: mpi , numba , jax , test , doc
Classifiers
Report project as malware

Project description

ForgeFF: semi-empirical potential fitting in Python

PyPI version Downloads GitHubActions

ForgeFF is a Python toolkit for fitting semi-empirical interatomic potentials. It keeps the model equations, parameter order, and fitting workflow explicit so the fundamentals stay easy to inspect and explain.

The recommended user-facing format is TOML:

  • custom analytic pair potentials
  • built-in analytical forms such as Morse and double-Morse
  • tabulated EAM and ADP term blocks
  • beginner-friendly examples for training, evaluation, grading, and calculators

Project details

Verified details

These details have been verified by PyPI
Project links
GitHub Statistics
Maintainers
Avatar for prnvrvs from gravatar.com prnvrvs

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License Expression: MIT
    SPDX License Expression
  • Author: Pranav Kumar
  • Requires: Python >=3.11
  • Provides-Extra: mpi , numba , jax , test , doc
Classifiers

Release history Release notifications | RSS feed

1.1.6

1.1.5

1.1.4

1.1.3

1.1.2

1.1.1

1.1.0

1.0.3

1.0.2

1.0.1

This version

1.0.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

forgeff-1.0.0.tar.gz (7.6 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

forgeff-1.0.0-py3-none-any.whl (76.1 kB view details)

Uploaded Python 3

File details

Details for the file forgeff-1.0.0.tar.gz.

File metadata

  • Download URL: forgeff-1.0.0.tar.gz
  • Upload date:
  • Size: 7.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for forgeff-1.0.0.tar.gz
Algorithm Hash digest
SHA256 119fa3b2376409cb3389cee9aa76758ee5458bc46bf5bdd41f1300f938c4339b
MD5 67298d4a6c106e8345695681f8391e89
BLAKE2b-256 876a31ef8692e64d68c55a6c8cb0d9a453570043e67c9b00b1c56c61e64a4d75

See more details on using hashes here.

Provenance

The following attestation bundles were made for forgeff-1.0.0.tar.gz:

Publisher: pypi.yml on prnvrvs/ForgeFF

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

File details

Details for the file forgeff-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: forgeff-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 76.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for forgeff-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 5c5abf9a3a8ba2f24d4a906ee007777c5496c93652f3fd1e997e11fceed9212c
MD5 56b91e44719832bc868e3fd3255a5fa7
BLAKE2b-256 8ce048a1326ca4b2783a05e8e529956417f8a2f102c5ca1c2c70d231882ebcda

See more details on using hashes here.

Provenance

The following attestation bundles were made for forgeff-1.0.0-py3-none-any.whl:

Publisher: pypi.yml on prnvrvs/ForgeFF

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

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page