Tools (1-20 of 21)

1. SVR Machine Learning Workshop

   Tools | 08 Aug 2022 | Contributor(s):: Davis McGregor

   Introductory tutorial on support vector regression (SVR) machine learning, cross validation, and hyperparameter tuning.

2. Chemprop Demo

   Tools | 11 Apr 2022 | Contributor(s):: Kevin Greenman

   Demo of the Chemprop message-passing neural network package for the Hands-on Data Science and Machine Learning Training Series

3. Polymer Genetic Algorithm

   Tools | 05 Nov 2021 | Contributor(s):: Joseph D Kern

   Generalized genetic algorithm designed for materials discovery.

4. Autonomous Neutron Diffraction Explorer

   Tools | 01 Nov 2021 | Contributor(s):: Austin McDannald

   Autonomously control neutron diffraction experiments to discover order parameter.

5. MATLAB R2021a

   Tools | 09 Sep 2021 | Contributor(s):: Gen Sasaki, Lisa Kempler

   MATLAB is a programming and numeric computing platform to analyze data, develop algorithms, and create models.

6. Debugging Neural Networks

   Tools | 07 Aug 2021 | Contributor(s):: Rishi P Gurnani

   Debug common errors in neural networks.

7. ML-aided High-throughput screening for Novel Oxide Perovskite Discovery

   Tools | 15 Jul 2021 | Contributor(s):: Anjana Talapatra

   ML-based tool to discover novel oxide perovskites with wide band gaps

8. Bayesian optimization tutorial using Jupyter notebook

   Tools | 11 Jun 2021 | Contributor(s):: Hieu Doan, Garvit Agarwal

   Active learning via Bayesian optimization for materials discovery

9. A Hands-on Introduction to Physics-Informed Neural Networks

   Tools | 21 May 2021 | Contributor(s):: Atharva Hans, Ilias Bilionis

   A Hands-on Introduction to Physics-Informed Neural Networks

10. A Batch Reification/Fusion Optimization Framework for Bayesian-based Material Optimization

    Tools | 27 Apr 2021 | Contributor(s):: Richard Couperthwaite, Raymundo Arroyave

    This tool is a Bayesian optimization framework that allows for a combination of a multi-fidelity (Reification/Fusion) optimization approach with a Batch Bayesian Approach.

11. Materials Graph Network

    Tools | 27 Jan 2021 | Contributor(s):: Chi Chen, Yunxing Zuo

    Materials Graph Networks for molecule and crystal structure-property relationship modeling

12. Machine Learning Force Field for Materials

    Tools | 25 Jan 2021 | Contributor(s):: Chi Chen, Yunxing Zuo

    Machine learning force field for materials

13. SEM Image Segmentation Workshop

    Tools | 12 Jan 2021 | Contributor(s):: Aagam Rajeev Shah, Darren K Adams, Mitisha Surana, Ricardo Toro, Sameh H Tawfick, Elif Ertekin

    This tool introduces users to machine learning used to segment microscopy images

14. Machine Learning Defect Behavior in Semiconductors

    Tools | 10 Nov 2020 | Contributor(s):: Arun Kumar Mannodi Kanakkithodi, Rushik Desai (editor)

    Develop machine learning models to predict defect formation energies in chalcogenides

15. Gaussian Process Regression Model for Piezoelectric and Dielectric Constants in Gallium Nitride

    Tools | 03 Aug 2020 | Contributor(s):: Saswat Mishra, Karthik Guda Vishnu, Alejandro Strachan

    Gaussian Process Regression Model for Piezoelectric and Dielectric Constants in Gallium Nitride as a function of Strain and Aluminum doping

16. Parsimonious neural networks

    Tools | 09 Jul 2020 | Contributor(s):: Saaketh Desai, Alejandro Strachan

    Design and train neural networks in conjunction with genetic algorithms to discover equations directly from data

17. Hands-on Deep Learning for Materials

    Tools | 10 Jun 2020 | Contributor(s):: Saaketh Desai, Edward Kim, Vinay Hegde

    This tool introduces users to deep learning techniques such as convolutional neural networks and variational auto encoders from a materials standpoint

18. Unsupervised learning using dimensionality reduction via matrix decomposition

    Tools | 14 Apr 2020 | Contributor(s):: Michael N Sakano, Alejandro Strachan

    Learn PCA and NMF via chemistry example

19. Querying Data Repositories

    Tools | 03 Apr 2020 | Contributor(s):: Zachary D McClure, Alejandro Strachan

    Query database repositories using Python based APIs and tips for managing data

20. Citrine Tools for Materials Informatics

    Tools | 05 Dec 2019 | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan

    Jupyter notebooks for sequential learning in the context of materials design. Run your own models, explore various methods and adapt the notebooks to your needs.