Awesome Open Source

Programming Languages

Search results for material materials science

materials-science x

43 search results found

Pymatgen ⭐ 1,254

Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Matminer ⭐ 422

Data mining for materials science

Awesome Materials Informatics ⭐ 310

Curated list of known efforts in materials informatics = modern materials science

Python for Materials Machine Learning, Materials Descriptors, Machine Learning Force Fields, Deep Learning, etc.

Pycalphad ⭐ 250

CALPHAD tools for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria.

Deep and Machine Learning for Microscopy

Bestpractices ⭐ 142

Things that you should (and should not) do in your Materials Informatics research.

Materials Knowledge System in Python

Düsseldorf Advanced Material Simulation Kit (Read-only mirror)

Crystal Graph Neural Networks

Simplifying the discovery and usage of machine-learning ready datasets in materials science and chemistry

Predict materials properties using only the composition information!

ML4Chem: Machine Learning for Chemistry and Materials

MODNet: a framework for machine learning materials properties

Deep Learning the Chemistry of Materials From Only Elemental Composition for Enhancing Materials Property Prediction

Fitting thermodynamic models with pycalphad - https://doi.org/10.1557/mrc.2019.59

Text Mined Synthesis_public ⭐ 52

Codes for text-mined solid-state reactions dataset

Bgolearn ⭐ 49

A Bayesian global optimization package for material design ｜ Adaptive Learning | Active Learning

Materials Synthesis Generative Models ⭐ 47

Public release of data and code for materials synthesis generation

Thermo_pw ⭐ 47

Thermo_pw is a driver of quantum-ESPRESSO routines for the automatic computation of ab-initio material properties.

A Python package to work with material microstructures and 3d data sets

Ai4materials ⭐ 35

Deep learning for crystal-structure recognition and analysis of atomic structures

Openmaterial ⭐ 33

3D model exchange format with physical material properties for virtual development, test and validation of automated driving.

Thermoparser ⭐ 31

A tool for streamlining data analysis and visualisation for thermoelectrics and charge carrier transport in computational materials science.

Materials Word Embeddings ⭐ 27

Word2Vec model trained across 640k+ materials science journal articles

Distributed data processing routines for multidimensional photoemission spectroscopy (MPES)

Mpds Api ⭐ 23

Tutorials, notebooks, issue tracker, and website on the MPDS API: the data retrieval interface for the Materials Platform for Data Science

Composite laminates engineering simulations in Python.

Materials informatics framework for ab initio data repositories

Mathinmse.github.io ⭐ 15

Applied Matematical Methods in Materials Engineering

Pytopomat ⭐ 15

Python Topological Materials (pytopomat) is a code for easy, high-throughput analysis of topological materials.

Twod_materials ⭐ 14

High throughput workflow tools for characterizing 2D materials in VASP.

Materials Coord ⭐ 13

MaterialsCoord: infrastructure to benchmark crystal structure coordination algorithms

Machine Learning Package Targeted for Amorphous Materials.

Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.

A Scheil-Gulliver simulation tool using pycalphad.

Materials Designer ⭐ 9

A standalone React.js/Redux based web application for the design and visualization of atomistic materials structures. Used at Mat3ra.com and can be deployed in standalone mode.

Mse Machinelearning Notebooks ⭐ 8

Machine Learning Introduction for a Material Scientist

Materials.sh ⭐ 8

Smact_workflows ⭐ 7

Computational experiments using SMACT for materials design

Materials Project Tool : An useful tool for manipulating, analying and visualization input and output of materials simulation codes.

Basic info and tutorials for the GUI of the Materials Platform for Data Science (MPDS)

Metallurgy ⭐ 7

Calculates approximate properties of alloy compositions

JSON schemas and examples representing structural data, characteristic properties, modeling workflows and related data about materials standardizing the diverse landscape of information

Materials Design in Javascript (made.js). A JavaScript (Node) library allowing for the creation and manipulation of material structures from atoms up on the web.

Synthesis Api ⭐ 5

Codebase for Synthesis Project API server

Optimade Client ⭐ 5

Isomorphic TypeScript / JavaScript client to aggregate all the official Optimade providers

Quantum_esperanto ⭐ 5

Very fast parser for the XML logs produced with the VASP, Vienna Ab initio Simulation Package

Synthesis Paragraph Classifier ⭐ 5

Codes and models for "Semi-supervised machine-learning classification of materials synthesis procedures". (https://doi.org/10.1038/s41524-019-0204-1)

Constitutive ⭐ 5

Library that includes standard material models (phenomenological laws) as well as implementations of multiscale homogenization schemes for the modeling of microscopically heterogeneous materials.

Synthesis Database Public ⭐ 5

Codebase for compiling a database of materials syntheses

Tensorflow implementation of message passing neural networks for molecules and materials

A collaborative Mozilla science project to build a materials science database for calculation results using simulation package VASP

Related Searches

Javascript Material (3,145)

Angular Material (1,651)

Typescript Material (1,567)

Python Material (1,490)

Html Material (1,457)

Java Material (1,450)

Reactjs Material (1,402)

Jupyter Notebook Material (1,207)

Css Material (913)

C Plus Plus Material (439)

1-43 of 43 search results

Privacy | About | Terms | Follow Us On Twitter

Copyright 2018-2024 Awesome Open Source. All rights reserved.