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Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?
14 Aug 2017 | Presentation Materials | 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...
Predicting Locations of Pollution Sources using Convolutional Neural Networks
07 Aug 2017 | Presentation Materials | Contributor(s): Yiheng Chi, Guang Lin, Nickolas D Winovich
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...
Model Selection Using Gaussian Mixture Models and Parallel Computing
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27 Jul 2016 | Tools | Contributor(s): Tian Qiu, Yiyi Chen, Georgios Karagiannis, Guang Lin
Model Selection Using Gaussian Mixture Models
Gaussian process regression in 1D
04 Dec 2014 | Tools | Contributor(s): Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve
Use Gaussian processes to represent x-y data
Random Forest Model Objects for Pulmonary Toxicity Risk Assessment
17 Apr 2013 | Downloads | Contributor(s): Jeremy M Gernand
This download contains MATLAB treebagger or Random Forest (RF) model objects created via meta-analysis of nanoparticle rodent pulmonary toxicity experiments. The ReadMe.txt file contains object...
Model Validation Document for "A Meta-Analysis of Carbon Nanotube Pulmonary Toxicity Studies – How Physical Dimensions and Impurities Affect the Toxicity of Carbon Nanotubes"
19 Nov 2012 | Papers | Contributor(s): Jeremy M Gernand, Elizabeth Casman
This document contains model learning statistics, and structure of the models utilized in the paper “A meta-analysis of carbon nanotube pulmonary toxicity studies – How physical dimensions and...