JOSS Papers: Material Learning

Published Papers

published Published 3 months ago

Chemiscope 1.0: interactive exploration of atomistic data from analysis to dissemination

TypeScript JavaScript

10.21105/joss.10380

published Published 9 months ago

exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design

Python

10.21105/joss.08879

published Published over 1 year ago

AutoEIS: Automated equivalent circuit modeling from electrochemical impedance spectroscopy data using statistical machine learning

Python

10.21105/joss.06256

published Published almost 2 years ago

cifkit: A Python package for coordination geometry and atomic site analysis

Python Jupyter Notebook

10.21105/joss.07205

published Published about 2 years ago

f3dasm: Framework for Data-Driven Design and Analysis of Structures and Materials

Python

@mpvanderschelling

10.21105/joss.06912

published Published about 2 years ago

SAMBA: A Trainable Segmentation Web-App with Smart Labelling

Python ImageJ Macro JavaScript TypeScript

10.21105/joss.06159

published Published over 2 years ago

Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science

Python

10.21105/joss.05467

published Published almost 3 years ago

brains-py, A framework to support research on energy-efficient unconventional hardware for machine learning

Python

10.21105/joss.05573

published Published about 4 years ago

UnlockNN: Uncertainty quantification for neural network models of chemical systems

Python PureBasic

10.21105/joss.03700

published Published over 4 years ago

spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.

Python

10.21105/joss.03781

Displaying papers 1 - 10 of 11 in total

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