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1دورية أكاديمية
المؤلفون: Justin K. Kirkland, Jugal Kumawat, Maliheh Shaban Tameh, Tyson Tolman, Allison C. Lambert, Graham R. Lief, Qing Yang, Daniel H. Ess
مصطلحات موضوعية: Biophysics, Biochemistry, Molecular Biology, Science Policy, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, predicting zirconocene properties, naturally emerge showing, machine learning algorithm, ligand aromatic carbons, feature importance analysis, calculated zirconocene properties, aromatic ligand framework, machine learning models, despite robust models, performance significantly depends, barriers zr metallocenes, models depends, transition states, smooth overlap, quantitative accuracy, persistence images, moderate influence, ethylene polymerization, energy structures, direct connections, coulomb matrices, chemical descriptors, bonding hapticity
الإتاحة: https://doi.org/10.1021/acs.jcim.3c01575.s001Test
https://figshare.com/articles/journal_contribution/Machine_Learning_Models_for_Predicting_Zirconocene_Properties_and_Barriers/25047812Test -
2
المؤلفون: Justin K. Kirkland, Jugal Kumawat, Maliheh Shaban Tameh, Tyson Tolman, Allison C. Lambert, Graham R. Lief, Qing Yang, Daniel H. Ess
مصطلحات موضوعية: Biophysics, Biochemistry, Molecular Biology, Science Policy, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, predicting zirconocene properties, naturally emerge showing, machine learning algorithm, ligand aromatic carbons, feature importance analysis, calculated zirconocene properties, aromatic ligand framework, machine learning models, despite robust models, performance significantly depends, barriers zr metallocenes, models depends, transition states, smooth overlap, quantitative accuracy, persistence images, moderate influence, ethylene polymerization, energy structures, direct connections, coulomb matrices, chemical descriptors, bonding hapticity
الإتاحة: https://doi.org/10.1021/acs.jcim.3c01575.s004Test
https://figshare.com/articles/dataset/Machine_Learning_Models_for_Predicting_Zirconocene_Properties_and_Barriers/25047821Test -
3
المؤلفون: Justin K. Kirkland, Jugal Kumawat, Maliheh Shaban Tameh, Tyson Tolman, Allison C. Lambert, Graham R. Lief, Qing Yang, Daniel H. Ess
مصطلحات موضوعية: Biophysics, Biochemistry, Molecular Biology, Science Policy, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, predicting zirconocene properties, naturally emerge showing, machine learning algorithm, ligand aromatic carbons, feature importance analysis, calculated zirconocene properties, aromatic ligand framework, machine learning models, despite robust models, performance significantly depends, barriers zr metallocenes, models depends, transition states, smooth overlap, quantitative accuracy, persistence images, moderate influence, ethylene polymerization, energy structures, direct connections, coulomb matrices, chemical descriptors, bonding hapticity
الإتاحة: https://doi.org/10.1021/acs.jcim.3c01575.s002Test
https://figshare.com/articles/dataset/Machine_Learning_Models_for_Predicting_Zirconocene_Properties_and_Barriers/25047815Test -
4
المؤلفون: Justin K. Kirkland, Jugal Kumawat, Maliheh Shaban Tameh, Tyson Tolman, Allison C. Lambert, Graham R. Lief, Qing Yang, Daniel H. Ess
مصطلحات موضوعية: Biophysics, Biochemistry, Molecular Biology, Science Policy, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, predicting zirconocene properties, naturally emerge showing, machine learning algorithm, ligand aromatic carbons, feature importance analysis, calculated zirconocene properties, aromatic ligand framework, machine learning models, despite robust models, performance significantly depends, barriers zr metallocenes, models depends, transition states, smooth overlap, quantitative accuracy, persistence images, moderate influence, ethylene polymerization, energy structures, direct connections, coulomb matrices, chemical descriptors, bonding hapticity
الإتاحة: https://doi.org/10.1021/acs.jcim.3c01575.s003Test
https://figshare.com/articles/dataset/Machine_Learning_Models_for_Predicting_Zirconocene_Properties_and_Barriers/25047818Test -
5
المؤلفون: Justin K. Kirkland, Jugal Kumawat, Maliheh Shaban Tameh, Tyson Tolman, Allison C. Lambert, Graham R. Lief, Qing Yang, Daniel H. Ess
مصطلحات موضوعية: Biophysics, Biochemistry, Molecular Biology, Science Policy, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, predicting zirconocene properties, naturally emerge showing, machine learning algorithm, ligand aromatic carbons, feature importance analysis, calculated zirconocene properties, aromatic ligand framework, machine learning models, despite robust models, performance significantly depends, barriers zr metallocenes, models depends, transition states, smooth overlap, quantitative accuracy, persistence images, moderate influence, ethylene polymerization, energy structures, direct connections, coulomb matrices, chemical descriptors, bonding hapticity
الإتاحة: https://doi.org/10.1021/acs.jcim.3c01575.s005Test
https://figshare.com/articles/dataset/Machine_Learning_Models_for_Predicting_Zirconocene_Properties_and_Barriers/25047824Test