Outlier-Based Domain of ApplicabilityIdentification for Materials PropertyPrediction Models

01 February 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Machine learning models have been widely applied for material property prediction. However, practical application of these models can be hindered by a lack of information about how well they will perform on previously unseen types of materials. Because machine learning model predictions depend on the quality of the available training data, different domains of the material feature space are predicted with different accuracy levels by such models. The ability to identify such domains enables the ability to find the confidence level of each prediction, to determine when and how the model should be employed depending on the prediction accuracy requirements of different tasks, and to improve the model for domains with high errors. In this work, we propose a method to find domains of applicability using a large feature space and also introduce analysis techniques to gain more insight into the detected domains and subdomains.

Keywords

Domain of Applicability
Machine Learning
Outlier Detection

Supplementary materials

Title
Description
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Title
Supporting Information: Outlier-Based Domain of Applicability Identification for Materials Property Prediction Models
Description
Data preparation details, KMeans clsutering, Unsupervised Anomaly Detection, Complexities of PNNL, Cui and Delaney datasets, Sample molecules from subdomains.
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Supplementary weblinks

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