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![](/files/MTRjZGIzZDFkMzI4NjdhNjg4ZDFjM2E4OGFiYzIyMDMxM2VkYWMzMTk3OThiODFkYTUwNTIwNTEwNTQwMzU1MjovdmFyL3d3dy9uYW5vaHViL2FwcC9zaXRlL21lbWJlcnMvNDQ2Njc0L3RodW1iLnBuZw==)
Xander Mensah
https://nanohub.org/members/446674
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A Machine Learning Aided Hierarchical Screening Strategy for Materials Discovery
Online Presentations | 09 Sep 2021 | Contributor(s):: Anjana Talapatra
In this tutorial, we illustrate this approach using the example of wide band gap oxide perovskites. We will sequentially search a very large domain space of single and double oxide perovskites to identify candidates that are likely to be formable, thermodynamically stable, exhibit insulator...
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ML-aided High-throughput screening for Novel Oxide Perovskite Discovery
Tools | 15 Jul 2021 | Contributor(s):: Anjana Talapatra
ML-based tool to discover novel oxide perovskites with wide band gaps
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Constructing Accurate Quantitative Structure-Property Relationships via Materials Graph Networks
Online Presentations | 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.
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Materials Graph Network
Tools | 27 Jan 2021 | Contributor(s):: Chi Chen, Yunxing Zuo
Materials Graph Networks for molecule and crystal structure-property relationship modeling
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Machine Learning Defect Behavior in Semiconductors
Tools | 10 Nov 2020 | Contributor(s):: Arun Kumar Mannodi Kanakkithodi, Rushik Desai (editor)
Develop machine learning models to predict defect formation energies in chalcogenides