Collection 

Machine Learning of Defects in Crystals

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Open
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Machine learning (ML) has emerged as a powerful tool for studying the properties of condensed matter. To date, most research has focused on the bulk properties of solids, however, defects are ubiquitous in crystalline systems. Many modern functional technologies are constrained or enabled by their presence. For example, in photovoltaics, point-defects can introduce electronic states that fall within the band gap and result in voltage losses through trapping and non-radiative recombination. In contrast, point-defect qubits are an emerging platform for quantum computing and sensing that are uniquely enabled by long lived and addressable spin states on paramagnetic point defects. Defects pose a particular challenge for modern machine learning methods since they are often charged, leading to long range forces that are not well captured by existing approaches. This collection focuses on the development and application of novel machine learning approaches to study the geometric, thermodynamic, kinetic, and electronic properties of defects in the solid state.
 

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The Collection will publish original research papers, and articles in various formats (full details on content types can be found here). Papers will be published in npj Computational Materials as soon as they are accepted and then collected together and promoted on the Collection homepage. All Collections are associated with a call for papers and are managed by one or more journal editors and/or Guest Editors.

This Collection welcomes submissions from all authors – and not by invitation only – on the condition that the manuscripts fall within the scope of the Collection and of npj Computational Materials more generally. All submissions are subject to the same peer review process and editorial standards as regular npj Computational Materials articles, including the journal’s policy on competing interests. The Editors declare no competing interests with the submissions which they have handled through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editor who has no competing interests. For more information, refer to our Collections guidelines.

This Collection is not supported by sponsorship.