noegroup/ScoreMD
A framework for training energy-based diffusion models for sampling and energy estimation.
This project helps computational chemists and molecular modelers perform consistent and accurate molecular dynamics simulations. It takes input on molecular structures or potential energy landscapes, and outputs both independent molecular samples and continuous simulation trajectories. Researchers in molecular science can use this to explore molecular behavior and energy landscapes.
Use this if you need a single model to both generate new molecular configurations and run dynamic simulations, ensuring consistency between sampling and energy estimation.
Not ideal if you primarily need to analyze existing simulation data or if you require direct quantum mechanical calculations for your molecular system.
Stars
94
Forks
7
Language
Python
License
MIT
Category
Last pushed
Nov 21, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/noegroup/ScoreMD"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
yang-song/score_sde_pytorch
PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential...
ermongroup/ncsnv2
The official PyTorch implementation for NCSNv2 (NeurIPS 2020)
yang-song/score_sde
Official code for Score-Based Generative Modeling through Stochastic Differential Equations...
amazon-science/unconditional-time-series-diffusion
Official PyTorch implementation of TSDiff models presented in the NeurIPS 2023 paper "Predict,...
AI4HealthUOL/SSSD-ECG
Repository for the paper: 'Diffusion-based Conditional ECG Generation with Structured State Space Models'