Presentation Materials (1-7 of 7)

1. Machine Learning in Materials - Center for Advanced Energy Studies and Idaho National Laboratory

   Presentation Materials | 24 Sep 2020 | Contributor(s):: Alejandro Strachan

   his hands-on tutorial will introduce participants to modern tools to manage, organize, and visualize data as well as machine learning techniques to extract information from it. ...

2. nanoHUB: Online Simulation and Data

   Presentation Materials | 24 Sep 2020 | Contributor(s):: Alejandro Strachan

   These slides introduce nanoHUB, an open platform for online simulations and collaboration.

3. Machine Learning for Property Prediction and Materials Discovery

   Presentation Materials | 20 Sep 2019 | 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

   Presentation Materials | 20 Aug 2019 | 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. Deep Machine Learning for Machine Performance and Damage Prediction

   Presentation Materials | 08 Aug 2018 | Contributor(s):: Elijah Reber, Nickolas D Winovich, Guang Lin

   Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at...

6. Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?

   Presentation Materials | 14 Aug 2017 | Contributor(s):: Hanjing Xu, Pradeep Kumar Gurunathan

   Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions...

7. Predicting Locations of Pollution Sources using Convolutional Neural Networks

   Presentation Materials | 07 Aug 2017 | Contributor(s):: Yiheng Chi, Nickolas D Winovich, Guang Lin

   Pollution is a severe problem today, and the main challenge in water pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or...