Stars
Torch-native C++/CUDA library to accelerate tensor-product layers in MLIPs
SO3krates and Universal Pairwise Force Field for Molecular Simulation
✨ 一站式 LLM 聊天机器人平台及开发框架 ✨ 支持 QQ、QQ频道、Telegram、企微、飞书、钉钉 | 知识库、MCP 服务器、OpenAI、DeepSeek、Gemini、硅基流动、月之暗面、Ollama、OneAPI、Dify
cuEquivariance is a math library that is a collective of low-level primitives and tensor ops to accelerate widely-used models, like DiffDock, MACE, Allegro and NEQUIP, based on equivariant neural n…
A package to compute the heat flux for MACE machine-learned force fields
This GitHub repository contains additional information supporting published manuscripts
Train, fine-tune, and manipulate machine learning models for atomistic systems
PhasesResearchLab / YPHON
Forked from yiwang62/YphonPackageYPHON is a package that calculates phonon properties using a mixed-space approach with force constants derived from VASP.
Evaluation of universal machine learning force-fields https://doi.org/10.1021/acsmaterialslett.5c00093
Self-describing sparse tensor data format for atomistic machine learning and beyond
DFTB+ general package for performing fast atomistic simulations
simple GNN potential version 2
Efficient and easy to use fortran implementation of the Ewald summation method
Automation software for calculating anharmonic phonon properties
A tool for computing Raman Spectra from Molecular Dynamics
AMLP integrates dataset creation, input/output handling, and analysis for machine learning interatomic potentials. It supports Gaussian, VASP, and CP2K, with LLM agents for code selection and ASE-b…
A high performace ReaxFF/AIMD trajectory analysis tool based on graph theory.
Python codes for calculation of polarization displacement vector in ferroelectric materials
Benchmarking machine learning interatomic potentials with Grüneisen parameter.
Heat-conductivity benchmark test for foundational machine-learning potentials
Assessment and Application of Universal Machine Learning Interatomic Potentials in Solid-State Electrolyte Research
Tutorials on atomic simulations related to my research
An AiiDA workflow that implements a fully automated active learning scheme to train a neural network interatomic potential
Brillouin zones and paths for dispersion calculations in Julia.