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Materials Informatics,

Edition 1 Molecules, Crystals, and Beyond

Editors: By Krishna Rajan

Publication Date: 29 May 2026

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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.

Key Features

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)

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 .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

Materials Informatics,

Editors: By Krishna Rajan

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About the author

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