1. Machine learning for high entropy atomic properties

   26 Oct 2021 | Contributor(s): Mackinzie S Farnell, Zachary D McClure, Alejandro Strachan

   Explore machine learning models used to assess the variations in local atomic properties in high entropy alloys.

2. 3 min Research Talk: Using Machine Learning for Materials Discovery and Property Prediction

   26 Sep 2019 | Online Presentations | Contributor(s): Mackinzie S Farnell

   Machine Learning models present a transformative method of optimization and prediction in science and engineering research. In the chemical sciences, unsupervised deep learning models such as autoencoders have shown to be useful for property prediction and material...

3. Machine Learning for Property Prediction and Materials Discovery

   20 Sep 2019 | Presentation Materials | Contributor(s): Mackinzie S Farnell, Nicolae C Iovanac, Brett Matthew Savoie

   Machine learning displays excellent potential for generating material property predictions and discovering novel compounds with desirable properties; however, it can be prohibitively costly to obtain data to train machine learning models. This barrier can be overcome by training models to...

4. Web-based Machine Learning Tool for Material Discovery and Property Prediction

   20 Aug 2019 | Presentation Materials | Contributor(s): Bryan Arciniega, Mackinzie S Farnell, Nicolae C Iovanac, Brett Matthew Savoie

   Machine Learning models present a transformative method of optimization and prediction in science and engineering research. In the chemical sciences, unsupervised deep learning models such as autoencoders have shown to be useful for property prediction and material...

5. Chemical Autoencoder for Latent Space Enrichment

   19 Sep 2019 | Tools | Contributor(s): Bryan Arciniega, Mackinzie S Farnell, Nicolae C Iovanac, Brett Matthew Savoie

   Chemical Autencoder uses machine learning for property prediction

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