Machine learning methods are changing the way we design and discover new materials. This book provides an overview of approaches successfully used in addressing materials problems (alloys, ferroelectrics, dielectrics) with a focus on probabilistic methods, such as Gaussian processes, to accurately estimate density functions. The authors, who have extensive experience in this interdisciplinary field, discuss generalizations where more than one competing material property is involved or data with differing degrees of precision/costs or fidelity/expense needs to be considered.
About the Author
Los Alamos National Laboratory, Los Alamos, New Mexico University of Virginia, Charlottesville, Virgina Santa Fe, New Mexico Santa Fe, New Mexico
- Contributor: Ghanshyam Pilania
- Imprint: Morgan & Claypool Publishers
- ISBN13: 9781681737379
- Number of Pages: 188
- Packaged Dimensions: 191x235mm
- Format: Paperback
- Publisher: Morgan & Claypool Publishers
- Release Date: 2020-03-30
- Series: Synthesis Lectures on Materials and Optics
- Binding: Paperback / softback
- Biography: Los Alamos National Laboratory, Los Alamos, New Mexico University of Virginia, Charlottesville, Virgina Santa Fe, New Mexico Santa Fe, New Mexico
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