Autonomous Symmetry Analysis and Structure Prototyping for Materials Discovery

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2019

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Abstract

The structure-property relationship is the foundation for materials modeling, predicting the behavior of compounds based on structural characteristics. With the advancement of ab initio methods and high performance computing, atomic configurations are being explored at an unprecedented rate. To effectively navigate the vast search space, procedures are presented for analyzing and prototyping crystalline compounds for high-throughput simulation. Integrated into the Automatic Flow (AFLOW) framework for computational materials discovery, these tools are the underlying workhorse for symmetry classification and materials generation. In particular, algorithms are detailed for determining the set of isometries for crystals, featuring a comprehensive collection of symmetry descriptions along with routines to handle ill-conditioned structural data. A library of crystallographic structures is also introduced — showcasing nearly 600 prototypes with representatives from each space group — and is complemented with functionality for rapidly creating materials via prototype decoration. Lastly, a module for comparing crystalline compounds is described to identify duplicate entries within large data sets and detect novel structure-types, independent of representation. Mechanisms are featured for converting geometries into a standard prototype convention, providing a direct pathway for incorporation into the crystallographic library. With these autonomous computational approaches, compounds are automatically classified and generated, enabling the design of new and structurally distinct materials.

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Hicks, David Jonathan (2019). Autonomous Symmetry Analysis and Structure Prototyping for Materials Discovery. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/20114.

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Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license.