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Articles 51721 - 51750 of 294741
Full-Text Articles in Physical Sciences and Mathematics
Mgrre_Thinsections_Mgrre-112_15, Mgrre
Mgrre_Thinsections_Mgrre-112_16, Mgrre
Mgrre_Thinsections_Mgrre-112_17, Mgrre
Mgrre_Thinsections_Mgrre-112_18, Mgrre
Mgrre_Thinsections_Mgrre-113_1, Mgrre
Mgrre_Thinsections_Mgrre-113_3, Mgrre
Mgrre_Thinsections_Mgrre-113_4, Mgrre
Mgrre_Thinsections_Mgrre-113_5, Mgrre
Mgrre_Thinsections_Mgrre-113_6, Mgrre
Mgrre_Thinsections_Mgrre-111_5, Mgrre
Mgrre_Thinsections_Mgrre-113_7, Mgrre
Mgrre_Thinsections_Mgrre-114_3, Mgrre
Mgrre_Thinsections_Mgrre-114_5, Mgrre
Mgrre_Thinsections_Mgrre-114_8, Mgrre
Mgrre_Thinsections_Mgrre-114_9, Mgrre
Mgrre_Thinsections_Mgrre-114_11, Mgrre
Mgrre_Thinsections_Mgrre-114_14, Mgrre
Mgrre_Thinsections_Mgrre-114_15, Mgrre
Mgrre_Thinsections_Mgrre-114_19, Mgrre
Mgrre_Thinsections_Mgrre-114_18, Mgrre
Electronic, Redox, And Optical Property Prediction Of Organic Π-Conjugated Molecules Through A Hierarchy Of Machine Learning Approaches, Vinayak Bhat, Parker Sornberger, Balaji Sesha Sarath Pokuri, Rebekah Duke, Baskar Ganapathysubramanian, Chad Risko
Electronic, Redox, And Optical Property Prediction Of Organic Π-Conjugated Molecules Through A Hierarchy Of Machine Learning Approaches, Vinayak Bhat, Parker Sornberger, Balaji Sesha Sarath Pokuri, Rebekah Duke, Baskar Ganapathysubramanian, Chad Risko
Chemistry Faculty Publications
Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated …
Electronic, Redox, And Optical Property Prediction Of Organic Π-Conjugated Molecules Through A Hierarchy Of Machine Learning Approaches, Vinayak Shantaram Bhat, Parker Sornberger, Balaji Sesha Sarath Pokuri, Rebekah Duke, Baskar Ganapathysubramanian, Chad Risko
Electronic, Redox, And Optical Property Prediction Of Organic Π-Conjugated Molecules Through A Hierarchy Of Machine Learning Approaches, Vinayak Shantaram Bhat, Parker Sornberger, Balaji Sesha Sarath Pokuri, Rebekah Duke, Baskar Ganapathysubramanian, Chad Risko
Chemistry Faculty Publications
Accelerating the development of π-conjugated molecules for applications such as energy generation and storage, catalysis, sensing, pharmaceuticals, and (semi)conducting technologies requires rapid and accurate evaluation of the electronic, redox, or optical properties. While high-throughput computational screening has proven to be a tremendous aid in this regard, machine learning (ML) and other data-driven methods can further enable orders of magnitude reduction in time while at the same time providing dramatic increases in the chemical space that is explored. However, the lack of benchmark datasets containing the electronic, redox, and optical properties that characterize the diverse, known chemical space of organic π-conjugated …
Mgrre_Thinsections_Mgrre-110_9, Mgrre
Mgrre_Thinsections_Mgrre-112_1, Mgrre
Mgrre_Thinsections_Mgrre-110_16, Mgrre
Mgrre_Thinsections_Mgrre-110_19, Mgrre
Mgrre_Thinsections_Mgrre-114_4, Mgrre
Mgrre_Thinsections_Mgrre-114_7, Mgrre