Artificial Intelligence in Material Genomics


Artificial Intelligence in Material Genomics

Joy Hoskeri H, Nivedita Pujari S, Badrinath Kulkarni, Arun K. Shettar

The invention of new materials with desired properties is always a matter of interest. The material genome projects and material genome initiative have bought new insights into material genomics. Artificial intelligence (AI) is the decision-making ability of a computable machine. AI in material genomics has stimulated the field of material science. AI tools like Atom2vec, MATLAB, ICSD, MPIinterfaces, PyCDT, and AFLOWLIB have opened ways for the discovery of various materials. These AI tools and databases are also efficient in the property prediction of new materials, improvement in characterization protocols, experimental parameter standardization, fastening simulation scale, development of high throughput methods, and data analysis. The current chapter is focused on AI-based developments in the material genomics.

Material Genomics, Artificial Intelligence, Machine Learning, AI Tools, and Databases

Published online , 18 pages

Citation: Joy Hoskeri H, Nivedita Pujari S, Badrinath Kulkarni, Arun K. Shettar, Artificial Intelligence in Material Genomics, Materials Research Foundations, Vol. 147, pp 87-104, 2023


Part of the book on Application of Artificial Intelligence in New Materials Discovery

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