Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures-Reference-Cited by-同舟云学术

Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures

Published:2020-04 Issue: Volume:188 Page:92-100
ISSN:1359-6454
Container-title:Acta Materialia
language:en
Short-container-title:Acta Materialia

Author:

Alcobaça Edesio^ORCID,Mastelini Saulo Martiello,Botari Tiago,Pimentel Bruno Almeida,Cassar Daniel Roberto^ORCID,de Carvalho André Carlos Ponce de Leon Ferreira^ORCID,Zanotto Edgar Dutra^ORCID

Funder

São Paulo State Research Foundation

Publisher

Elsevier BV

Subject

Metals and Alloys,Polymers and Plastics,Ceramics and Composites,Electronic, Optical and Magnetic Materials

Reference47 articles.

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3. Unsupervised word embeddings capture latent knowledge from materials science literature;Tshitoyan;Nature,2019

4. Semi-supervised machine-learning classification of materials synthesis procedures;Huo;npj Computational Materials,2019

5. Can glass stability parameters infer glass forming ability?;Nascimento;Journal of Non-Crystalline Solids,2005

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