Machine Learning Models for Predicting Polymer Solubility in Solvents across Concentrations and Temperatures

Artificial intelligence and machine learning have become essential tools in predicting material properties to aid in the accelerated design of new materials. Polymer solubility, critical for new formulations and solution processing, is one such property. However, current models are limited by inadeq...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Mona Amrihesari (20421304) (author)
مؤلفون آخرون:	Joseph Kern (15628383) (author), Hilary Present (20421307) (author), Sofia Moreno Briceno (20421310) (author), Rampi Ramprasad (1355760) (author), Blair Brettmann (11883511) (author)
منشور في:	2024
الموضوعات:	Biophysics Biochemistry Medicine Pharmacology Biotechnology Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified process designers working predicting material properties crystal16 turbidity measurements become essential tools many features contributing experimental transmission data temperatures artificial intelligence predicting polymer solubility industrial polymer solutions machine learning models solvents across concentrations machine learning polymer solutions polymer solubility many temperatures data set current models solution processing nonsolvent classes new materials new formulations multiple concentrations cannot capture accelerated design
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Machine Learning Models for Predicting Polymer Solubility in Solvents across Concentrations and Temperatures

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