Predicting crystal structure by merging data mining with quantum mechanics
Source abstract
Abstract
Modern methods of quantum mechanics have proved to be effective tools to understand and even predict materials properties. An essential element of the materials design process, relevant to both new materials and the optimization of existing ones, is knowing which crystal structures will form in an alloy system. Crystal structure can only be predicted effectively with quantum mechanics if an algorithm to direct the search through the large space of possible structures is found. We present a new approach to the prediction of structure that rigorously mines correlations embodied within experimental data and uses them to direct quantum mechanical techniques efficiently towards the stable crystal structure of materials.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Materials Characterization Techniques · Ion-surface interactions and analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (24)
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