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...
Main Authors: | Zhu, Ruiming, Tian, Siyu Isaac Parker, Ren, Zekun, Li, Jiali, Buonassisi, Tonio, Hippalgaonkar, Kedar |
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Other Authors: | School of Materials Science and Engineering |
Format: | Journal Article |
Language: | English |
Published: |
2023
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/168721 |
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