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Limitations of machine learning models when predicting compounds with completely new chemistries: possible improvements applied to the discovery of new non-fullerene acceptors

We try to determine if machine learning (ML) methods, applied to the discovery of new materials on the basis of existing data sets, have the power to predict completely new classes of compounds (extrapolating) or perform well only when interpolating between known materials. We introduce the leave-on...

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Detalles Bibliográficos
Autores principales: Zhao, Zhi-Wen, del Cueto, Marcos, Troisi, Alessandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: RSC 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189862/
https://www.ncbi.nlm.nih.gov/pubmed/35769202
http://dx.doi.org/10.1039/d2dd00004k