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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...
https://nanohub.org/resources/26904
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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...
https://nanohub.org/resources/26948
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S Kiran Kadam
https://nanohub.org/members/172030
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IPython Notebooks for Machine Learning
Collections |
21 May 2017 |
Posted by Tanya Faltens
https://nanohub.org/groups/ncnure2017/collections/technical-resources
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Dedy Farhamsa
https://nanohub.org/members/170299
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Claire Battye
https://nanohub.org/members/169180
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Marius Stan
https://nanohub.org/members/156713
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Model Selection Using Gaussian Mixture Models and Parallel Computing
27 Jul 2016 | Tools | Contributor(s): Tian Qiu, Yiyi Chen, Georgios Karagiannis, Guang Lin
Model Selection Using Gaussian Mixture Models
https://nanohub.org/resources/msugmmpc
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Lukasz Burzawa
https://nanohub.org/members/147258
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Juan Sebastian Martinez
I am a senior in Electronic Engineering and Systems and Computer Engineering at Universidad de los Andes in Bogotá. Throughout my learning, I have gained experience with different programming...
https://nanohub.org/members/145729
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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
https://nanohub.org/resources/gptool
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Ilias Bilionis
Dr. Ilias Bilionis is an Assistant Professor at the School of Mechanical Engineering, Purdue University. His research is motivated by energy and material science applications and it focuses on the...
https://nanohub.org/members/107467
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Rohit Kaushal Tripathy
https://nanohub.org/members/106614
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German Felipe Giraldo
https://nanohub.org/members/85538
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Ahmed-Amine Homman
https://nanohub.org/members/82074
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anupam ghosh
M.Sc. Physics (2007), 1 year experience in neuroscience (2008-09), 1.5 yrs experience in synthesis and characterization of Nickel nano-wires (2010-11), 1 year experience in simulation of...
https://nanohub.org/members/81944
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Mahika Dubey
University of Illinois Urbana Champaign Class of 2016 (Urbana, IL) B.S. Computer Engineering (Department of ECE) Minor in Statisics, iFoundry Innovation Certificate Program Monta Vista High School...
https://nanohub.org/members/80897
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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...
https://nanohub.org/resources/17539
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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...
https://nanohub.org/resources/15901
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Jeremy M Gernand
Jeremy Gernand is currently an Assistant Professor of Industrial Health and Safety at Penn State University. He is currently pursuing research related to the occupational hazards of exposure to...
https://nanohub.org/members/61288