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.
| Autorius: | Ghanshyam Pilania, Turab Lookman, James E. Gubernatis, Prasanna V. Balachandran, |
| Serija: | Synthesis Lectures on Materials and Optics |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2020 |
| Knygos puslapių skaičius: | 192 |
| ISBN-10: | 3031012550 |
| ISBN-13: | 9783031012556 |
| Formatas: | 235 x 191 x 11 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods“
