In this book chapter, we discuss how the problem of accelerated materials discovery is related to other computational problems in artificial intelligence, such as computational creativity, concept learning, and invention, as well as to machine-aided discovery in other scientific domains. These connections lead, mathematically, to the emergence of three classes of algorithms that are inspired largely by the approximation-theoretic and machine learning problem of dimensionality reduction, by the information-theoretic problem of data compression, and by the psychology and mass communication problem of holding human attention. The possible utility of functionals including dimension, information [measured in bits], and Bayesian surprise [measured in wows], emerge as part of this description, in addition to measurement of quality in the domain.

Original languageEnglish (US)
Title of host publicationSpringer Series in Materials Science
Number of pages14
StatePublished - 2018

Publication series

NameSpringer Series in Materials Science
ISSN (Print)0933-033X

ASJC Scopus subject areas

  • Materials Science(all)


Dive into the research topics of 'Dimensions, bits, and wows in accelerating materials discovery'. Together they form a unique fingerprint.

Cite this