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Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?
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...
Gaussian process regression in 1D
26 Nov 2014 | | Contributor(s):: Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve
Use Gaussian processes to represent x-y data
Model Selection Using Gaussian Mixture Models and Parallel Computing
20 Jul 2016 | | Contributor(s):: Tian Qiu, Yiyi Chen, Georgios Karagiannis, Guang Lin
Model Selection Using Gaussian Mixture Models
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 | | 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 impurities affect the toxicity of carbon nanotubes.” This information is meant to supplement and...
Predicting Locations of Pollution Sources using Convolutional Neural Networks
28 Jul 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...
Random Forest Model Objects for Pulmonary Toxicity Risk Assessment
09 Apr 2013 | | 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 descriptions including output definitions, input parameter descriptions, and applicable limits.