Predicting synthesizability using machine learning on databases of existing inorganic materials

• المؤلفون الرئيسيون: Zhu, Ruiming, Tian, Siyu Isaac Parker, Ren, Zekun, Li, Jiali, Buonassisi, Tonio, Hippalgaonkar, Kedar
• مؤلفون آخرون: School of Materials Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Materials; Crystals; Energy
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/168721

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...