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Meeting 2017 TMS Annual Meeting & Exhibition
Symposium Computational Materials Discovery and Optimization – From Bulk to Materials Interfaces and 2D Materials
Presentation Title An Unsupervised Pattern Recognition Approach for Local Structural Analysis of Condensed Matter
Author(s) Arash Dehghan Banadaki, Jason Maldonis, Paul Voyles, Srikanth Patala
On-Site Speaker (Planned) Arash Dehghan Banadaki
Abstract Scope The local arrangement of atoms in solid state matter is arguably the most important signature of the materials that influences a broad range of mechanical and functional properties. However, description and quantification of the local structure is non-trivial and the automated analysis of such descriptors for large sets of data is lacking. In this talk we present a point-pattern matching algorithm that can efficiently detect the instances of a template structure in a given set of atom coordinates. In addition, we introduce a metric that can uniquely quantify the amount of distortion in a set of atom coordinates with respect to an underlying template. We will demonstrate how the combination of the proposed algorithm and the distortion metric can be utilized in an unsupervised machine learning framework for identifying distinct local atomic environments (e.g. those corresponding to distinct phases) in atomistic simulations of heterogeneous bulk materials.
Proceedings Inclusion? Definite: None Selected

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Automation and Database of First Principles Phonon Calculations
Band Gap Opening in 2D Bi-layered Silicon Film
Benchmarking and Validation of Density Functional Theory for Solids
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