A Gibbs energy minimization based on the CALPHAD approach

Project description

Equilipy

Equilipy is an open-source python package that offers multicomponent multiphase equilibrium calculations based on the CALPHAD (CALculation of PHAse Diagram) approach. With a set of Gibbs energy description (Thermochemical database) and input conditions (Composition, temperature, pressure), equilibrium phase configureation, amount, composition, and thermochemical properties can be obtained. Equilipy uses the Gibbs energy descriptions furnished by THERMOCHIMICA with the modified Gibbs energy minimization algorithm initially proposed by de Capitani, C. and Brown, T.H. (1987).

Check out documentation for further description.

Dependencies

Dependency	Version	Required	Libraries
Fortran	-	Yes	-
Python	3.9+	Yes	numpy, wheel, meson, ninja

Installation

Installation using pip is available for Equilipy.

pip install equilipy

Features and example

The following features are currently available.

Single condition equilibrium calculations
Batch equilibrium calculations
Scheil-Gulliver solidification
Phase selection Hehehe

For details, check out the example directory and Features and Examples

Citing Equilipy

If you use Equilipy in your work, please cite the following paper. In addition, cite the current release or version used from Zenodo.

Contributing

We encourage you to contribute to Equilipy. Please see contributing guidelines.

Additional note

Examples in Equilipy uses polars dataframe for fast data processing. In particular, example 3 requires fastexcel as the optional dependancy in polars. Install fastexcel via

pip install fastexcel

Additionally, if you are using large dataset (> 4billion), install

pip install polars-u64-idx

If you are using old CPUs, install

pip install polars-lts-cpu

For details, check out polars dependencies.

Project details

Release history Release notifications | RSS feed

This version

0.2.1

Jan 7, 2026

0.2.0

Nov 21, 2025

0.1.8

Oct 22, 2025

0.1.7

Oct 21, 2025

0.1.6

Oct 17, 2025

0.1.4

Jul 18, 2024

0.1.3

May 22, 2024

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equilipy 0.2.1

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MD5	`e18d5bbc6ab05bdc448cc5b56600983b`
BLAKE2b-256	`f85b708d091fc908bb9c8eacb0f7eb373080db3ad8821cacd9e05a719572e8bd`

Algorithm	Hash digest
SHA256	`124c5403cc2b574692ab52a072222c1149325bad2d888db725fe4c722b7b44af`
MD5	`7bffdc2036c0d67f86cbb85765f5d941`
BLAKE2b-256	`94de391a268c23d834f68a95120ccf11ef2638579659e4a659c4458ba718b7d0`

Algorithm	Hash digest
SHA256	`4e73066fcee1fff76087fa6174a964d832c064eeaf2801bffff45915f5af9635`
MD5	`318b2062f2074700da95c6e863506855`
BLAKE2b-256	`f9ccbbdcad6f64fc4d09b402fffebcb4089b03e7f8d3342b528f98a5f6543ab2`

Algorithm	Hash digest
SHA256	`aa0219d15fcd6685ab1cf7e6aa65cdaa32185c0c2aa6dbfb59e662fbba477f0b`
MD5	`bf1cc934c6e55c9e8791673541e385de`
BLAKE2b-256	`87f690feadb548bf22538dae56ee2f952f020a0ddc8f4837a0edcf8458139110`

Algorithm	Hash digest
SHA256	`57aeb41351886708a7a2189cfcca796650db758357ab8af677f6275de7e3a3df`
MD5	`fd112eac653843c880a1534a5cccd0d9`
BLAKE2b-256	`b8358c1c54f460ade2ec893c46ec6ab22abceac78636d64e9d0859acb3be5abe`

Algorithm	Hash digest
SHA256	`7bb87f691c185a426f441ff302f7ada98a542709258cbbec084cea08f3bbe847`
MD5	`db1d6b2284ec59403a40e6f3f11f6e8a`
BLAKE2b-256	`16d8716321b0c951565283df23619037919acb3fd73ed85fc49a2b3121fa9cae`

Algorithm	Hash digest
SHA256	`385919785161bd8752886f1b4ff65f47f96c61e170a74f2444e314dd49bdd031`
MD5	`e6d1f8c431b04fd495a6e5f59b9892a7`
BLAKE2b-256	`42726308e1636c6508f0fccb1283642f978df51063fd98a2d66da3f1a3ed4aa4`

Algorithm	Hash digest
SHA256	`5bb6bcd580d05869b13666b6bd2bc47ecfea91233a3e592dc98eaf073e9d8c8c`
MD5	`74f7c9b491843f6338d6682a8dd30ae8`
BLAKE2b-256	`2d383a2fd9fa22d52df369b56c3bff4aec59bf9d879d4030dfc14d53c9d6fb2e`