elastic_vasp

This package is created as python library to facilitate density functional theory based package VASP to calculate Second-order elastic constants from strain-energy relationship. This tool is only compatible with DFT package VASP.  

For installing this Library using pip

pip install elastic-vasp

ForgeFF

ForgeFF is an open-source Python framework designed to make developing accurate, high-performance interatomic potentials simple, flexible, and reproducible. It provides a complete workflow for fitting semi-empirical interatomic potentials such as EAM, ADP, SW, and Tersoff, as well as custom symbolic potentials, to reference atomic data including energies, forces, and stresses.

✔ Multiple Calculation Backends — Simple plugin to Atomic Simulation Environment
Choose the backend that suits your needs:

• NumPy — easy to use and portable

• Numba — JIT-accelerated for fast performance

• SymPy — symbolic differentiation engine for custom potentials

Switching between backends requires no code changes.

✔ Flexible Optimization Algorithms
ForgeFF integrates several proven optimization strategies:

• L-BFGS-B

• Nelder–Mead

• Simulated Annealing

• Built-in Genetic Algorithm

✔ Integrated Active Learning
Grade configuration space dynamically using the Maximum Volume MaxVol algorithm

pip install git+https://github.com/prnvrvs/ForgeFF.git

MOTEP

MOTEP is an open-source Python framework designed to make developing accurate, high-performance interatomic potentials simple, flexible, and reproducible. It provides a complete workflow for fitting Moment Tensor Potentials (MTPs) or similar machine-learning potentials to reference atomic data such as energies, forces, and stresses.

✔ Multiple Calculation Backends (Simple plugin to Atomic simulation environment)

Choose the backend that suits your needs:

NumPy — easy to use and portable

Numba — JIT-accelerated for fast performance

mlip-based engine — compatibility with existing MLIP workflows

Switching between backends requires no code changes.

✔ Flexible Optimization Algorithms

MOTEP integrates several proven optimization strategies:

L-BFGS-B, Nelder–Mead, Differential Evolution, Dual Annealing, Built-in Genetic Algorithm

pip install git+https://github.com/imw-md/motep.git