bigd4/PyNEP
A python interface of NEP
This tool helps computational materials scientists and physicists analyze and predict material properties using machine learning. It takes atomistic simulation data and outputs calculated energies, forces, and descriptors, facilitating research in materials science. It is designed for researchers who use atomistic simulations to study material behaviors.
Use this if you need to integrate machine learning potentials (specifically NEP) into your atomistic simulations to calculate material properties like energy, forces, and phonon-related data.
Not ideal if you are looking for a general-purpose machine learning library or a tool for molecular dynamics simulations that does not rely on the NEP potential.
Stars
68
Forks
18
Language
C++
License
MIT
Category
Last pushed
Oct 27, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/bigd4/PyNEP"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
SimonBlanke/Gradient-Free-Optimizers
Lightweight optimization with local, global, population-based and sequential techniques across...
Gurobi/gurobi-machinelearning
Formulate trained predictors in Gurobi models
emdgroup/baybe
Bayesian Optimization and Design of Experiments
heal-research/pyoperon
Python bindings and scikit-learn interface for the Operon library for symbolic regression.
simon-hirsch/ondil
A package for online distributional learning.