beingujjwalraj/Multiscale-Modelling-of-Material-Using-Machine-Learning
This repository demonstrates multiscale modeling of copper heat pipes using machine learning, integrating grain-scale data with FEA via a UMAT. It highlights grain size’s impact on stress, strain, and heat transfer for optimized material design.
No commits in the last 6 months.
Stars
8
Forks
6
Language
Jupyter Notebook
License
—
Category
Last pushed
Dec 05, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/beingujjwalraj/Multiscale-Modelling-of-Material-Using-Machine-Learning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deepmodeling/deepmd-kit
A deep learning package for many-body potential energy representation and molecular dynamics
chemprop/chemprop
Message Passing Neural Networks for Molecule Property Prediction
deepchem/deepchem
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
mir-group/nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling...