Machine Learning Constrained with Dimensional Analysis and Scaling Laws: Simple, Transferable, and Interpretable Models of Materials from Small Datasets

Machine learning (ML) from materials databases can accelerate the design and discovery of new materials through the development of accurate, computationally inexpensive models to predict materials properties. These models in turn enable rapid screening of large materials search space. However, mater...

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Veröffentlicht in:	Chemistry of materials 2019-01, Vol.31 (2), p.314-321
Hauptverfasser:	Kumar, Narendra, Rajagopalan, Padmini, Pankajakshan, Praveen, Bhattacharyya, Arnab, Sanyal, Suchismita, Balachandran, Janakiraman, Waghmare, Umesh V
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
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