Toward Interpretable Machine Learning Models for Materials Discovery

Toward Interpretable Machine Learning Models for Materials Discovery

Machine learning (ML) and artificial intelligence (AI) methods for modeling useful materials properties are now important technologies for rational design and optimization of bespoke functional materials. Although these methods make good predictions of the properties of new materials, current modeli...

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Bibliographic Details
Main Authors: Paulius Mikulskis, Morgan R. Alexander, David Alan Winkler
Format: Article
Language:English
Published: Wiley 2019-12-01
Series:Advanced Intelligent Systems
Subjects:
interpretability
machine learning
materials designs
molecular descriptors
structure–property relationships
Online Access:https://doi.org/10.1002/aisy.201900045

Internet

https://doi.org/10.1002/aisy.201900045

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