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...
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...
Overview of Computational Methods and Machine Learning: Panel Discussion
Online Presentations | 14 Jun 2019 | Contributor(s): Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin
The individual Panel Talks which accompanies this discussion can be found here.Why do we need experiments?Are your methods “descriptive” or “predictive”?Do you work with any other theory/simulation groups?On the 5 year timescale: is machine-learning hype or a real...
Overview of Computational Methods and Machine Learning: Panel Talks
The Panel Discussion which follows these individual presentations can be found here.Individucal Presentations:Theory and Machine Learning in the Chemical Sciences, Brett Matthew Savoie;Divide and Conquer with QM/MM Methods, Pradeep Kumar Gurunathan;Computational Chemistry/Materials, Peilin...
Chemical Autoencoder for Latent Space Enrichment
Tools | 19 Sep 2019 | 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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