Predicting synthesizability using machine learning on databases of existing inorganic materials

Predicting synthesizability using machine learning on databases of existing inorganic materials

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: School of Materials Science and Engineering
Format: Article
Language:English
Published: 2023
Subjects:
Engineering::Materials
Crystals
Energy
Online Access:https://hdl.handle.net/10356/168721
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Institution: Nanyang Technological University
Language: English

Internet

https://hdl.handle.net/10356/168721

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