... Using Machine Learning and the Materials Project. Comput. Mater. Sci. 177, 109614. doi:10.1016/j.commatsci.2020.109614 Long, T., Fortunato, N. M., Opahle, I., Zhang, Y., Samathrakis, I., Shen, C., et al. (2020). CCDCGAN: Inverse Design ...
With its various application examples to micro- and macrostructure mechanics, this is an invaluable resource for mechanical engineers as well as for researchers wanting to improve on this method and extend its outreach.
Nanotechnology for Microelectronics and Optoelectronics outlines in detail the fundamental solid-state physics concepts that explain the new properties of matter caused by this reduction of solids to the nanometer scale.
Presents new methods and techniques for analysis and optimum design of materials at the microstructure level Authors' methodology introduces spectral approaches not available in previous texts, such as the incorporation of crystallographic ...
This book describes the modern real-space approach to electronic structures and properties of crystalline and non-crystalline materials in a form readily accessible to undergraduates in materials science, physics, and chemistry.
This book fills a gap by presenting our current knowledge and understanding of continuum-based concepts behind computational methods used for microstructure and process simulation of engineering materials above the atomic scale.
The book is about mathematical and computational foundations of texture analysis. Numerical techniques are indispensable in texture analysis, so the book is primarily addressed to researchers and students using these techniques in practice.