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A Machine Learning-based Atom Contribution Method

Employing efficient and validated model-based methods to first quickly identify promising solvent candidates that could then be verified through focused experiments is interesting option worth considering.

Important issues are highlighted in this paper published in the AIChE journal. Congratulations to the Thai Team of PSE for SPEED for participating in this project. Congratulations to all authors (Qilei Liua, Lei Zhang, Kun Tanga, Linlin Liua, Jian Dua, Qingwei Meng, and Rafiqul Gani) for an excellent contribution to the work.

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