xwxw66's Stars
mushroomfire/mdapy
A simple and fast python library to handle the data generated from molecular dynamics simulations
blaiszik/Materials-Databases
libAtoms/matscipy
Materials science with Python at the atomic-scale
russlanj/Superhardness-Data
All files including the code, data and crystal files used in our paper "Crystal structure guided machine learning for the discovery and design of intrinsically hard materials" that is published in Journal of Materiomics
bru32/CoolProp
Thermophysical properties for the masses
agiliopadua/lammps
Public development project of the LAMMPS MD software package
MariaPanoukidou/Zero-shear-viscosity
Green Kubo and Einstein-Helfand methods for calculation of viscosity (DPD specific)
callee2006/MachineLearning
Deep learning and machine learning example codes for practice
austinmcdannald/feasst
FEASST: Free Energy and Advanced Sampling Simulation Toolkit (prototype of https://pages.nist.gov/feasst)
drcassar/viscosity-graybox-nn
Gray-box Neural Network to predict the viscosity of liquids
drcassar/shap
A game theoretic approach to explain the output of any machine learning model.
drcassar/glasspy
Python module for scientists working with glass materials
drcassar/SciGlass
The database contains a vast set of data on the properties of glass materials.
kimjonghokr/MCMC
Implementation of Markov Chain Monte Carlo in Python from scratch
kimjonghokr/viscnet
Machine learning model to predict the viscosity of oxide liquids
kimjonghokr/machine_learning_glass
kimjonghokr/ANDiE-v1_0
ANDiE: Autonomous Neutron Diffraction Explorer
Vovadoes/CalculationDynamicViscosity
VictorOliveiraCortes/Viscosity-Calculation
kimjonghokr/GFA_viscosity
Glass Forming Ability Calculation (Jezica)
Dezhao-Huang/Lammps-shear-viscosity-calculation
This repository contains the lammps custom script to calculate the shear viscosity using the Couette flow and the Green-Kubo relationship.
google-deepmind/materials_discovery
nkaria1/cellular_automata
we have a matrix with N columns and M rows. Each entry in the matrix is either 0 or 1. We always initialize Row 1. Given a rule , we construct the subsequent row by applying the rule to the previous row and so on. We can extend the number of rows indefinitely. Guided by one of the rules of cellular automata described : http://www.wolframscience.com/nksonline/page-53 Also added the feature to locate all occurances of a specific pattern. The pattern is based on 6 matrix elements located in two subsequent rows. This kind of analysis finds application in pattern recognition (growth of crystals in snowflakes, development of patterns in sea shells, improving unclear images, tracing mutaion)
DayaKishor/Cellular-Automata-Simulation-of-Crystal-Growth
The program will calculate the basic nature of growth of the thin film.
plumed-tutorials/plumed-tutorials
The repository for storing the lessons for the PLUMED tutorials site
msultan/SML_CV
Using supervised machine learning to build collective variables for accelerated sampling
y1xiaoc/deepmd-plumed
using deepmd models as CVs in plumed
Vilgefortzz/multiscale-modelling
App for the visualization of grain growth using Cellular Automata, Monte carlo and MC static recrystallization algorithms
GraGLeS/GraGLeS2D
A simulation software for anisotropic grain growth in 2D
androsan/CAFE
Python script for numerical simulation of grain-growth during solidification of 3D printed metals