DC Field | Value | Language |
---|---|---|
dc.contributor.author | Liu, Qilei | ko |
dc.contributor.author | Zhang, Lei | ko |
dc.contributor.author | Tang, Kun | ko |
dc.contributor.author | Liu, Linlin | ko |
dc.contributor.author | Du, Jian | ko |
dc.contributor.author | Meng, Qingwei | ko |
dc.contributor.author | Gani, Rafiqul | ko |
dc.date.accessioned | 2021-03-04T06:30:11Z | - |
dc.date.available | 2021-03-04T06:30:11Z | - |
dc.date.created | 2020-11-30 | - |
dc.date.issued | 2021-02 | - |
dc.identifier.citation | AICHE JOURNAL, v.67, no.2, pp.e17110 | - |
dc.identifier.issn | 0001-1541 | - |
dc.identifier.uri | http://hdl.handle.net/10203/281200 | - |
dc.description.abstract | Solvents are widely used in chemical processes. The use of efficient model-based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic, environment and human health properties. In this paper, an optimization-based MLAC-CAMD framework is established for solvent design, where a novel machine learning-based atom contribution method is developed to predict molecular surface charge density profiles (sigma-profiles). In this method, weighted atom-centered symmetry functions are associated with atomic sigma-profiles using a high-dimensional neural network model, successfully leading to a higher prediction accuracy in molecular sigma-profiles and better isomer identifications compared with group contribution methods. The new method is integrated with the computer-aided molecular design technique by formulating and solving a mixed-integer nonlinear programming model, where model complexities are managed with a decomposition-based strategy. Finally, two case studies involving crystallization and reaction are presented to highlight the wide applicability and effectiveness of the MLAC-CAMD framework. | - |
dc.language | English | - |
dc.publisher | WILEY | - |
dc.title | Machine learning-based atom contribution method for the prediction of surface charge density profiles and solvent design | - |
dc.type | Article | - |
dc.identifier.wosid | 000588153100001 | - |
dc.identifier.scopusid | 2-s2.0-85096751500 | - |
dc.type.rims | ART | - |
dc.citation.volume | 67 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | e17110 | - |
dc.citation.publicationname | AICHE JOURNAL | - |
dc.identifier.doi | 10.1002/aic.17110 | - |
dc.contributor.nonIdAuthor | Liu, Qilei | - |
dc.contributor.nonIdAuthor | Zhang, Lei | - |
dc.contributor.nonIdAuthor | Tang, Kun | - |
dc.contributor.nonIdAuthor | Liu, Linlin | - |
dc.contributor.nonIdAuthor | Du, Jian | - |
dc.contributor.nonIdAuthor | Meng, Qingwei | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | atom contribution | - |
dc.subject.keywordAuthor | computer-aided molecular design | - |
dc.subject.keywordAuthor | decomposition-based algorithm | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | surface charge density profiles (sigma-profiles) | - |
dc.subject.keywordPlus | AIDED MOLECULAR DESIGN | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | IBUPROFEN | - |
dc.subject.keywordPlus | CHEMISTRY | - |
dc.subject.keywordPlus | DATABASE | - |
dc.subject.keywordPlus | PRODUCT | - |
dc.subject.keywordPlus | CAMD | - |
dc.subject.keywordPlus | TOOL | - |
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