Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials

Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials

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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) algorithms provide a ne...

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Bibliographic Details
Main Authors: Zhu, Ruiming, Tian, Siyu Isaac Parker, Ren, Zekun, Li, Jiali, Buonassisi, Tonio, Hippalgaonkar, Kedar
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering
Format: Article
Language:English
Published: American Chemical Society (ACS) 2023
Online Access:https://hdl.handle.net/1721.1/150804

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https://hdl.handle.net/1721.1/150804

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