Informing Mechanical Model Development Using Lower-Dimensional Descriptions of Lattice Distortion

Pagan, Darren C.; Schmidt, Gideon H.; Borum, Andy; Long, Timothy J.; Beaudoin, Armand Joseph

doi:10.1007/s40192-020-00196-y

Cited by 2 publications

(3 citation statements)

References 37 publications

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“…Many materials scientists are interested in deriving simple analytical equations [73][74][75][76][77][78][79] , which can also be considered as one kind of surrogate model. Simple analytical equations are usually less accurate than their black-box counterparts.…”

Section: Machine Learning Explainability In Materials Sciencementioning

confidence: 99%

“…ML can help with each of the three steps. State variables can be found by performing feature selection or dimension reduction on the original data [73][74][75][76]81 . Functional forms can be screened automatically using tools like Eureqa 78,79 and SISSO 32,82 .…”

Section: Machine Learning Explainability In Materials Sciencementioning

confidence: 99%

“…Functional forms can be screened automatically using tools like Eureqa 78,79 and SISSO 32,82 . Variable coefficients can be predicted directly from an ML model or estimated by performing a least square fit [73][74][75] .…”

Section: Machine Learning Explainability In Materials Sciencementioning

confidence: 99%

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Explainable machine learning in materials science

et al. 2022

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Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.

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