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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 scattering...
https://nanohub.org/members/81944
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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...
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Asep Ridwan Setiawan
https://nanohub.org/members/282014
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Autonomous Neutron Diffraction Experiments with ANDiE
14 Nov 2021 | | Contributor(s):: Austin McDannald
This tutorial will cover the working principles of ANDiE, how physics was encoded into the design, and demonstrate how ANDiE can be used to autonomously control neutron diffraction experiments.
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Autonomous Neutron Diffraction Explorer
01 Nov 2021 | | Contributor(s):: Austin McDannald
Autonomously control neutron diffraction experiments to discover order parameter.
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Aytekin Gel
https://nanohub.org/members/327168
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Bakhtiyor Rasulev
https://nanohub.org/members/305866
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Barry Sanders
Dr. Barry Sanders is Director of the Institute for Quantum Science and Technology at the Univer- sity of Calgary, Lead Investigator of the Alberta Major Innovation Fund Project on Quantum Tech-...
https://nanohub.org/members/269902
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Batch Reification Fusion Optimization (BAREFOOT) Framework
09 Jun 2021 | | Contributor(s):: Richard Couperthwaite
This tutorial will present the fundamentals of multi-fidelity fusion as well as Sequential and Batch Bayesian Optimization as possible optimization approaches that can be integrated with high accuracy computational models or experimental procedures to speed up the optimization or design of...
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Bayesian optimization tutorial using Jupyter notebook
11 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal
Active learning via Bayesian optimization for materials discovery
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Big Data in Reliability and Security: Applications
30 May 2019 | | Contributor(s):: Saurabh Bagchi
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Big Data in Reliability and Security: Some Basics
30 May 2019 | | Contributor(s):: Saurabh Bagchi
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Bryan Arciniega
Bryan Arciniega is a third year undergraduate at California State Polytechnic University, Pomona who is studying computer engineering and finance. He currently works as an IT technician for Cal...
https://nanohub.org/members/230182
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Chemical Autoencoder for Latent Space Enrichment
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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Chemprop Demo
11 Apr 2022 | | Contributor(s):: Kevin Greenman
Demo of the Chemprop message-passing neural network package for the Hands-on Data Science and Machine Learning Training Series
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Citrine Tools for Materials Informatics
05 Dec 2019 | | Contributor(s):: Juan Carlos Verduzco Gastelum, Alejandro Strachan
Jupyter notebooks for sequential learning in the context of materials design. Run your own models, explore various methods and adapt the notebooks to your needs.
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Claire Battye
"Research is creating new knowledge."Neil Armstrong
https://nanohub.org/members/169180
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Classical Computing with Topological States: Coping with a post-Moore World
21 Jun 2021 | | Contributor(s):: Avik Ghosh
There are two examples I will focus on ? one is doing conventional Boolean logic at low power below the thermal Boltzmann limit, using the topological properties of Dirac fermions to control transmission across a gated interface. The other is doing collective computing using temporal state...
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Feb 03 2021
Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
This tutorial covers materials graph networks for modeling crystal and molecular properties. We will introduce the graph representation of crystals and molecules and how the convolutional...
https://nanohub.org/events/details/1953
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Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
09 Mar 2021 | | Contributor(s):: Chi Chen
This tutorial covers materials graph networks for modeling crystal and molecular properties. We will introduce the graph representation of crystals and molecules and how the convolutional operations are carried out on the materials graphs.