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Representation of molecular structures with persistent homology for machine learning applications in chemistry

Chemistry

Representation of molecular structures with persistent homology for machine learning applications in chemistry

J. Townsend, C. P. Micucci, et al.

Discover how cutting-edge machine learning techniques are transforming the screening of functionalized molecules and materials! This innovative research, conducted by Jacob Townsend, Cassie Putman Micucci, John H. Hymel, Vasileios Maroulas, and Konstantinos D. Vogiatzis, showcases a unique molecular representation based on persistent homology, leading to the identification of promising CO₂-selective molecules from a vast database of organic compounds.

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