Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification
It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not been well explored in the field of heterogeneous ca...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Institute of Physics
2023
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Subjects: | |
Online Access: | View Fulltext in Publisher View in Scopus |