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An informatics software stack for point defect-derived opto-electronic properties: the Asphalt Project

Published online by Cambridge University Press:  02 September 2019

Jonathon N. Baker
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Suite 3002, Raleigh, NC 27695, USA
Preston C. Bowes
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Suite 3002, Raleigh, NC 27695, USA
Joshua S. Harris
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Suite 3002, Raleigh, NC 27695, USA
Douglas L. Irving
Affiliation:
Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Suite 3002, Raleigh, NC 27695, USA
Corresponding
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Abstract

Computational acceleration of performance metric-based materials discovery via high-throughput screening and machine learning methods is becoming widespread. Nevertheless, development and optimization of the opto-electronic properties that depend on dilute concentrations of point defects in new materials have not significantly benefited from these advances. Here, the authors present an informatics and simulation suite to computationally accelerate these processes. This will enable faster and more fundamental materials research, and reduce the cost and time associated with the materials development cycle. Analogous to the new avenues enabled by current first-principles-based property databases, this type of framework will open entire new research frontiers as it proliferates.

Type
Artificial Intelligence Prospectives
Copyright
Copyright © Materials Research Society 2019 

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