Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials

[Image: see text] Defining the metric for synthesizability and predicting new compounds that can be experimentally realized in the realm of data-driven research is a pressing problem in contemporary materials science. The increasing computational power and advancements in machine learning (ML) algor...

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Detalles Bibliográficos
Autores principales: Zhu, Ruiming, Tian, Siyu Isaac Parker, Ren, Zekun, Li, Jiali, Buonassisi, Tonio, Hippalgaonkar, Kedar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9996807/
https://www.ncbi.nlm.nih.gov/pubmed/36910925
http://dx.doi.org/10.1021/acsomega.2c04856

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9996807/
https://www.ncbi.nlm.nih.gov/pubmed/36910925
http://dx.doi.org/10.1021/acsomega.2c04856

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