Identifying Domains of Applicability of Machine Learning Models for Materials Science

23 December 2019, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


We present an extension to the usual machine learning process that allows for the identification of the domain of applicability of a fitted model, i.e., the region in its domain where it performs most accurately. This approach is applied to several vastly different but commonly used materials representations (namely the n-gram approach, SOAP, and the many body tenor representation), which are practically indistinguishable based on performance using a single error statistic. Moreover, these models appear unsatisfactory for screening applications as they fail to reliably identify the ground state polymorphs. When applying our newly developed analysis for each of the models, we can identify the domain of applicability for each model according to a simple set of interpretable conditions. We show that identification of the domain of applicability in the prediction of the formation energy enables a more accurate ground-state search - a crucial step for the discovery of novel materials.


machine learning model analysis
density functional theory
high-throughput screening
ground-state search

Supplementary materials

manuscript.Domain of Applicability


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