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Creators/Authors contains: "Oliynyk, Anton O."

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  1. ; ; ; ; ( , Physical Chemistry Chemical Physics)

    The support vector machine model produced the best results with a root mean square error of 1.54 × 10−6K−1. The model was applied to 3 593 726 possible AA′BB′O3compositions, resulting in 150 451 predictions in the model confidence region.

     
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    Free, publicly-accessible full text available November 29, 2024
  2. ; ; ( , Inorganic Chemistry)
    Free, publicly-accessible full text available July 17, 2024
  3. ; ; ; ( , Chemistry of Materials)
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  4. ; ; ( , Journal of Chemical Education)
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  5. ; ; ; ; ; ; ; ( , Chemistry of Materials)
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  6. ; ; ( , Computational Materials Science)
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  7. ; ; ; ; ( , Advanced Materials)
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  8. ; ; ( , Crystal Growth & Design)
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  9. ; ; ( , Annual Review of Materials Research)
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    The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmation of ML predictions, either via experiment or via physics-based simulations. In this review, we first identify the core components common to materials informatics discovery pipelines, such as training data, choice of ML algorithm, and measurement of model performance. Then we discuss some prominent examples of validated ML-driven materials discovery across a wide variety of materials classes, with special attention to methodological considerations and advances. Across these case studies, we identify several common themes, such as the use of domain knowledge to inform ML models. 
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  10. ; ; ; ; ( , Journal of the American Chemical Society)
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