Name: Materials Informatics
Price: 210 USD
Availability: InStock
ISBN: 9780443222566

Materials Informatics: Molecules, Crystals, and Beyond, 1st Edition is a textbook by Krishna Rajan that addresses how information science functions within materials discovery, design, and optimization. Written for advanced students, materials scientists, academic researchers, and industrial researchers, the volume establishes a structured pedagogical foundation for computational materials analysis across diverse academic and professional settings.

The book examines core discipline challenges such as data sparsity, sources of uncertainty, and multidimensional structure-property relationships. It details physics-informed and chemistry-informed data representations of material structure while presenting algorithmic tools for supervised and unsupervised machine learning. The text demonstrates machine learning applications aimed at targeted property screening alongside the prediction of material structures and physical properties.

As a defining feature, the volume outlines a pedagogical framework to reform materials science and engineering education using informatics principles. This 574-page text prepares scholars and practitioners to incorporate data-driven computational methods into academic instruction and ongoing laboratory research.

Chapter 1: Information science for materials science
- • 1.1 Information content and complexity
- • 1.2 Data dimensionality and multiscale information
- • 1.3 From uncertainty to insight in multiscale data
Chapter 2: Enabling knowledge discovery from data
- • 2.1 Computational toolkit
- • 2.2 Materials representations
Chapter 3: Informatics for thermodynamics: Stability to synthesizability
- • 3.1 Electronic structure
- • 3.2 Mapping phase equilbria
- • 3.3 Synthesizability
- • 3.4 Enabling hypothesis generation
Chapter 4: Pixels to properties
- • 4.1 Imaging: A high-dimensional data landscape
- • 4.2 Learning from images and spectra
- • 4.3 Data landscape for 3D atomic-scale imaging
Chapter 5: Combinatorial to autonomous experimentation
- • 5.1 Evolution of combinatorial experimental strategies
- • 5.2 The transition to autonomous experimentation
Chapter 6: Mapping connections and pathways
- • 6.1 Materials cartography
- • 6.2 Mapping the shape of data
- • 6.3 Networks for materials design
Chapter 7: Sustaining the growth of the materials informatics ecosystem
- • 7.1 Curation of data
- • 7.2 Guiding principles for databases
- • 7.3 Databases as recommendation engines
- • 7.4 Constants and bounds in material properties
Chapter 8: Shifting the educational paradigm: Discovering concepts and connections
- • 8.1 Enabling concept discovery in material science
- • 8.2 A pedagogical framework for materials informatics