Materials Informatics,
Edition 1 Molecules, Crystals, and BeyondEditors: By Krishna Rajan
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Description
Materials Informatics: Molecules, Crystals and Beyond discusses the role of information science in aiding the discovery and interpretation of multiscale relationships that are critical for materials discovery, design, and optimization. The book covers key challenges in applying information science methods to materials science, including the multidimensional nature of structure-property relationships, data sparsity, and the nature and sources of uncertainty, along with a brief overview of the algorithmic tools used for unsupervised and supervised learning.
Building on these topics, chapters then cover the development of physics/chemistry informed data representations of structure and properties, the application of machine learning for structure and property prediction and screening for targeted properties, and the utilization of techniques such a graphics recognition, natural language processing, and statistically driven visualization tools in deciphering processing-structure-property-performance relationships in materials.
Key Features
- Explores the nature of data curation from the perspective of both data production and consumption and the different paradigms of data management
- Highlights the role of informatics in correlative imaging for different genres of microscopy methods ranging from optical imaging to atomic-scale tomography
- Provides a framework for transforming materials science and engineering education through the lens of Materials Informatics
About the author
By Krishna Rajan, SUNY Distinguished Professor and Erich Bloch Chair, Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY, USA
1.1 Information content and complexity
1.2 Data dimensionality and multiscale information
1.3 From uncertainty to insight in multiscale data
2. Enabling knowledge discovery from data
2.1 Computational toolkit
2.2 Materials representations
3. Informatics for thermodynamics: Stability to synthesizability
3.1 Electronic structure
3.2 Mapping phase equilbria
3.3 Synthesizability
3.4 Enabling hypothesis generation
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
5. Combinatorial to autonomous experimentation
5.1 Evolution of combinatorial experimental strategies
5.2 The transition to autonomous experimentation
6. Mapping connections and pathways
6.1 Materials cartography
6.2 Mapping the shape of data
6.3 Networks for materials design
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
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
Advanced students, materials scientists, and academic and industrial researchers interested in materials informatics and materials design and discover
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