Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods - Prasanna V. Balachandran,James E. Gubernatis,Turab Lookman,Ghanshyam Pilania
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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 ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Prasanna V. Balachandran, James E. Gubernatis, Turab Lookman, Ghanshyam Pilania |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Series | Synthesis Lectures on Materials and Optics |
| Izlaides gads | 2020 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783031012556 |