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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
German Felipe Giraldo
IPython Notebooks for Machine Learning
21 May 2017 |
Posted by Tanya Faltens
Jeremy M Gernand
Jocelyn Teresia Dunn
Juan Sebastian Martinez
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...