Mutual information informed novelty estimation of materials along chemical and structural axes

Using mutual information to analyze inter-material distances, this work presents a parameter-free approach to quantify material novelty along chemical and structural axes through data-driven similarity criteria.

Authors

Taylor D. Sparks (ORCID: https://orcid.org/0000-0001-8020-7711)
Andrew Falkowski (ORCID: https://orcid.org/0000-0002-1633-8183)

Institutions

University of Utah (US)

Publication Details

Journal: Digital Discovery
Published: 2025-01-01
DOI: https://doi.org/10.1039/d5dd00167f
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 0.00

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article

Mutual information informed novelty estimation of materials along chemical and structural axes

Taylor D. Sparks, Andrew Falkowski

Jan 1, 2025

Digital Discovery

Machine Learning in Materials Science

article

Mutual information informed novelty estimation of materials along chemical and structural axes

Taylor D. Sparks, Andrew Falkowski

January 1, 2025article en

Abstract

Digital DiscoveryVol. 4(7)

University of Utah (US)

Openalex Percentile: Top 91%

Machine Learning in Materials Science

DOI Open Access PDF OpenAlex

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Mutual information informed novelty estimation of materials along chemical and structural axes — Taylor D. Sparks, Andrew Falkowski · Digital Discovery (2025) | TGRS Research Map | TGRS