Parity-LRX/FSCTEP
A Multi-Operator Equivariant Framework for High-Performance Machine Learning Force Fields, supporting External Fields embedding and Physical Tensors prediction.
This tool helps computational chemists and materials scientists predict properties of molecules and materials. It takes atomic structure data (like coordinates and atom types) and optionally external electric or magnetic fields as input. It then generates accurate predictions for physical properties such as charge, dipole moment, polarizability, and magnetic moment, which can be deployed directly into simulation software like LAMMPS.
Use this if you need to rapidly and accurately predict complex physical properties of atomic systems, especially when external fields are present or multi-fidelity training data is available.
Not ideal if you are looking for a simple, off-the-shelf solution for basic energy and force calculations without needing to predict physical tensors or incorporate external fields.
Stars
18
Forks
1
Language
Python
License
—
Category
Last pushed
Mar 11, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Parity-LRX/FSCTEP"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
pdebench/PDEBench
PDEBench: An Extensive Benchmark for Scientific Machine Learning
tum-pbs/PhiFlow
A differentiable PDE solving framework for machine learning
ArnauMiro/pyLowOrder
High performance parallel reduced order Modelling library
lettucecfd/lettuce
Computational Fluid Dynamics based on PyTorch and the Lattice Boltzmann Method
peterdsharpe/NeuralFoil
NeuralFoil is a practical airfoil aerodynamics analysis tool using physics-informed machine...