Toward Predicting Solubility of Arbitrary Solutes in Arbitrary Solvents: Prediction of Density and Refractive Index Using Machine Learning Algorithms with Global Sensitivity Analysis
- DOI
- 10.1021/acs.oprd.6c00166
- Published
- 2026-06-12
- Container
- Organic Process Research & Development
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.oprd.6c00166,
title = {Toward Predicting Solubility of Arbitrary Solutes in Arbitrary Solvents: Prediction of Density and Refractive Index Using Machine Learning Algorithms with Global Sensitivity Analysis},
author = {Brian Hu and Jingchen Zhai and Xiguang Qi and Xibing He and Nick X. Wang and Junmei Wang},
year = {2026},
journal = {Organic Process Research \& Development},
doi = {10.1021/acs.oprd.6c00166},
url = {https://doi.org/10.1021/acs.oprd.6c00166}
}RIS
TY - JOUR TI - Toward Predicting Solubility of Arbitrary Solutes in Arbitrary Solvents: Prediction of Density and Refractive Index Using Machine Learning Algorithms with Global Sensitivity Analysis AU - Brian Hu AU - Jingchen Zhai AU - Xiguang Qi AU - Xibing He AU - Nick X. Wang AU - Junmei Wang PY - 2026 JO - Organic Process Research & Development DO - 10.1021/acs.oprd.6c00166 UR - https://doi.org/10.1021/acs.oprd.6c00166 ER -
APA
Hu, B., Zhai, J., Qi, X., He, X., Wang, N. X., & Wang, J. (2026). Toward Predicting Solubility of Arbitrary Solutes in Arbitrary Solvents: Prediction of Density and Refractive Index Using Machine Learning Algorithms with Global Sensitivity Analysis. Organic Process Research & Development. https://doi.org/10.1021/acs.oprd.6c00166
Source records
- crossref · retrieved 2026-09-25T12:22:39.081Z