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Active Learning via Bayesian Optimization for Materials Discovery
25 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal
In this tutorial, we will demonstrate the use of active learning via Bayesian optimization (BO) to identify ideal molecular candidates for an energy storage application.
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Aluminum: a safe, economical, high energy density material for energy storage, transport and splitting water to make hydrogen on demand
30 Mar 2009 | | Contributor(s):: Jerry M. Woodall
In 1968, a team lead by the author discovered that liquid gallium saturated with aluminum at room temperature would split water into hydrogen gas, alumina and heat. More recently his current team has discovered that bulk, solid Al rich alloys will also split water in the same manner. Since 1) the...
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An Introduction to Rechargeable Batteries
27 Nov 2017 | | Contributor(s):: Vilas Pol
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Batteries - Challenges and Solutions
15 Jun 2017 | | Contributor(s):: Vilas Pol
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Battery Optimization
27 Jul 2016 | | Contributor(s):: Lefei Zhang, Guang Lin, Salar Safarkhani
Tool for modeling Porous Lithium-Ion Batteries for optimization and uncertainty quantification.
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Bayesian optimization tutorial using Jupyter notebook
09 Jun 2021 | | Contributor(s):: Hieu Doan, Garvit Agarwal
Active learning via Bayesian optimization for materials discovery
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Bifunctional Air Electrode Studies
01 Apr 2016 | | Contributor(s):: Brian Demczyk, C. T. Liu
This presentation outlines a number of fundamental and processing studies conducted on carbon-based bifunctional air electrodes
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Bridging Mechanics and Electrochemistry: Experiments and Modeling on Battery Materials
03 Oct 2018 | | Contributor(s):: Kejie Zhao
This talk focuses on the interplay of mechanics, such as large deformation, plasticity, and fracture, with chemical reactions in Li-ion batteries. I will discuss the theories of coupled diffusion and stress, stress regulated interfacial reactions, reactive flow, and corrosive fracture in...
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Challenges and Opportunities in the Design of Rechargeable Batteries
26 Feb 2016 | | Contributor(s):: R. Edwin García
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Common Materials for Uncommon Microsystems
15 Dec 2016 | | Contributor(s):: Babak Ziaie
2016 Adams Lecture, Mechanical Engineering, Purdue University
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Degradation in Rechargeable Li-Ion Batteries
17 Mar 2016 | | Contributor(s):: R. Edwin García
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DFT Study of Anisotropic Elastic Property of LiCoO2 During Lithium Intercalation and Deintercalation Process
06 Dec 2018 | | Contributor(s):: Lingbin Meng, Alejandro Strachan
Lithium cobalt oxide (LiCoO2) is a popular cathode material of lithium-ion batteries due to its excellent electrochemical properties. In this study, the anisotropic elastic property of LiCoO2 has been studied by comparing the ratio of C11 and C33 between LiCoO2 and CoO2. As a result, the...
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dualfoil.py Demonstration Video: User-Friendly Electrochemical Simulations for Porous Electrodes
10 Feb 2016 | | Contributor(s):: R. Edwin García
Companion demonstration video for the tool dualfoil.py.
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dualfoil.py Tutorial: User-Friendly Electrochemical Simulations for Porous Electrodes
10 Feb 2016 | | Contributor(s):: R. Edwin García
Tutorial lecture for the tool dualfoil.py.
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Dualfoil.py: Porous Electrochemistry for Rechargeable Batteries
22 Aug 2015 | | Contributor(s):: Lucas Darby Robinson, R. Edwin García
Open Source Friendly Graphical User Interface for the classic dualfoil (Newman) model
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Electro-Chemo-Mechanics of Electrodes in Li-ion Batteries
10 Nov 2017 | | Contributor(s):: Kejie Zhao
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Energy and Nanoscience A More Perfect Union
27 Mar 2009 | | Contributor(s):: Mark Ratner
Huge problems of energy and sustainability confront the science/engineering community, mankind, and our planet. The energy problem comes in many dimensions, including supply, demand, conservation, transportation, and storage. This overview will stress the nature of these problems, and offer a few...
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In-Situ Examination of Thermal Runaway in Lithium-ion Batteries under Dynamic Loading and at High Temperatures using Nanomechanical Raman Spectroscopy
28 Feb 2018 | | Contributor(s):: Vikas Tomar
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Module 7: Active Learning for Design of Experiments
29 Sep 2020 | | Contributor(s):: Alejandro Strachan, Juan Carlos Verduzco Gastelum
This module introduces active learning in the context of materials discovery with hands-on online simulations. Active learning is a subset of machine learning where the information available at a given time is used to decide what areas of space to explore next. In this module, we will explore...
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MSE 597GM Lecture 10: 2D and 3D Battery Architectures II
20 Sep 2012 |