Materials Informatics
Molecules, Crystals and Beyond
- 1st Edition - December 1, 2025
- Imprint: Elsevier
- Author: Krishna Rajan
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 2 5 6 - 6
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 2 5 5 - 9
Materials Informatics: Molecules, Crystals and Beyond discusses the role of information science in aiding the discovery and interpretation of multiscale relationships that are… Read more
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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.
- Explores the intersection of machine intelligence and robotics in experimental and computationally driven materials discovery and design
- Highlights experimental advances in materials synthesis, processing, and characterization to generate data that enables the harnessing of informatics methods
- Discusses the next generation of materials databases, built based on the paradigm of ‘FAIR’ principles (Findability, Accessibility, Interoperability, and Reusability)
Advanced students, materials scientists, and academic and industrial researchers interested in materials informatics and materials design and discover
1 .Data analytics for materials science: an overview
Data driven multiscale relationships: allometric equations to QSARs
Data dimensionality and manifold learning
Uncertainty and data sparsity in materials science
Machine learning tools: complexity vs explainability
2 .Data driven representations of materials structure
Descriptor development
Data featurization methods for molecules: molecular orbitals to molecular geometry,
Data representation of crystals: graphs and information partitioning methods
Microstructural representations
3. Informatics for materials thermodynamics and kinetics
First principles to chemical crystallography
Learning from structure maps and databases
Machine learning for defect dynamics and interfaces
Designing for processing and properties via informatics
4. Images to properties: an information continuum
Experimental techniques to enable correlative imaging and spectroscopy
Spectral and imaging informatics: probing structure-property relationships
5. Combinatorial experiments to autonomous laboratories
Integrating combinatorial experiments with computation
Informatics to guide discovery from autonomous laboratories
Tracking experimental workflow and design
6. Mapping Connections and Pathways
Machine learning for microstructural evolution and mechanics
Linking materials chemistry to device performance
Exploring for hidden structure-property relationships
Accelerating pathways for high entropy alloy/ ceramic design
Connecting functionality and sustainability in materials design
7. Enabling an Ecosystem for Materials Informatics
Quantum computing for materials science
The next generation of databases: FAIR and Just
Revolutionizing the educational paradigm
APPENDIX: A bibliographic guide of useful references on computational methods in statistics and machine learning
Data driven multiscale relationships: allometric equations to QSARs
Data dimensionality and manifold learning
Uncertainty and data sparsity in materials science
Machine learning tools: complexity vs explainability
2 .Data driven representations of materials structure
Descriptor development
Data featurization methods for molecules: molecular orbitals to molecular geometry,
Data representation of crystals: graphs and information partitioning methods
Microstructural representations
3. Informatics for materials thermodynamics and kinetics
First principles to chemical crystallography
Learning from structure maps and databases
Machine learning for defect dynamics and interfaces
Designing for processing and properties via informatics
4. Images to properties: an information continuum
Experimental techniques to enable correlative imaging and spectroscopy
Spectral and imaging informatics: probing structure-property relationships
5. Combinatorial experiments to autonomous laboratories
Integrating combinatorial experiments with computation
Informatics to guide discovery from autonomous laboratories
Tracking experimental workflow and design
6. Mapping Connections and Pathways
Machine learning for microstructural evolution and mechanics
Linking materials chemistry to device performance
Exploring for hidden structure-property relationships
Accelerating pathways for high entropy alloy/ ceramic design
Connecting functionality and sustainability in materials design
7. Enabling an Ecosystem for Materials Informatics
Quantum computing for materials science
The next generation of databases: FAIR and Just
Revolutionizing the educational paradigm
APPENDIX: A bibliographic guide of useful references on computational methods in statistics and machine learning
- Edition: 1
- Published: December 1, 2025
- Imprint: Elsevier
- Language: English
KR
Krishna Rajan
Krishna Rajan is the SUNY Distinguished Professor and Erich Bloch Chair of the Department of Materials Design and Innovation (MDI) at the University at Buffalo; with a joint appointment as Chief Scientist in the Energy Processes and Materials Division at Pacific Northwest National Laboratory (PNNL). He has pioneered the field of Materials Informatics and data driven discovery in materials science and engineering and its impact on characterization, processing, and modeling of materials. He has received numerous recognitions including the Alexander von Humboldt Award from Germany, the CSIRO- Australia Distinguished Visiting Scientist Award, the CNRS Visiting Professorship from France and the Presidential Lecture Award from the National Institute of Materials Science, Japan.
Dr Rajan received his undergraduate degree in Metallurgy and Materials Science from the University of Toronto followed by a doctorate in Materials Science from MIT with a minor in Science and Technology policy. He subsequently held post-doctoral appointments at MIT and Cambridge University. He was a staff scientist at the National Research Council of Canada, followed by faculty positions at Rensselaer Polytechnic Institute and Iowa State University before coming to the University at Buffalo as the founding chair of the MDI department. It is the first department that has its research and curriculum built around an informatics perspective of materials science and engineering.
Affiliations and expertise
SUNY Distinguished Professor and Erich Bloch Chair, Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY, USA