Summary
Functional, topologically complex organic molecules are rising stars in modern materials science due to their biocompatibility, structural variability, and wealth of physico-chemical properties. Their practical applications often involve interactions with small molecular targets (e.g., gases, environmental pollutants, and drugs) via relatively weak non-covalent forces. Key to these interactions are the topological features of host materials: arrangement of functional groups, pore size, and cavity volume.
Atom types and the forces connecting them in space determine molecular and material structures, defining their fundamental physical and chemical properties. These patterns comprise a universal chemical language. Numerous molecular representations exist, from strings in chemoinformatics to matrices in chemical machine learning. While these big data-oriented fingerprints generally reduce the dimensionality of atomic composition and connectivity, they do not capture the intricacies of shape and topology.
In PATTERNCHEM, several families of functional organic materials – graphenes, covalent-organic frameworks, and hyperbranched polymers – will provide a unique foundation for developing application-oriented fingerprints of their topological and non-covalent interaction features. After elucidating diverse structural descriptors of atomistic arrangement, substitution patterns, and two- and three-dimensional shapes of these materials, we will establish a scheme for quantifying the propensity for non-covalent interactions and assessing host-guest complementarity. Using this scheme, chemical and physical performance indicators relevant to targeted applications (e.g., as sensors, filters, and nanocarriers) can be computed. Finally, structure-property relationships between computed performance indicators and developed descriptors will be established and implemented into predictive frameworks for functional organic materials.
Atom types and the forces connecting them in space determine molecular and material structures, defining their fundamental physical and chemical properties. These patterns comprise a universal chemical language. Numerous molecular representations exist, from strings in chemoinformatics to matrices in chemical machine learning. While these big data-oriented fingerprints generally reduce the dimensionality of atomic composition and connectivity, they do not capture the intricacies of shape and topology.
In PATTERNCHEM, several families of functional organic materials – graphenes, covalent-organic frameworks, and hyperbranched polymers – will provide a unique foundation for developing application-oriented fingerprints of their topological and non-covalent interaction features. After elucidating diverse structural descriptors of atomistic arrangement, substitution patterns, and two- and three-dimensional shapes of these materials, we will establish a scheme for quantifying the propensity for non-covalent interactions and assessing host-guest complementarity. Using this scheme, chemical and physical performance indicators relevant to targeted applications (e.g., as sensors, filters, and nanocarriers) can be computed. Finally, structure-property relationships between computed performance indicators and developed descriptors will be established and implemented into predictive frameworks for functional organic materials.
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| Web resources: | https://cordis.europa.eu/project/id/101042290 |
| Start date: | 01-06-2022 |
| End date: | 31-05-2027 |
| Total budget - Public funding: | 1 492 821,00 Euro - 1 492 821,00 Euro |
Cordis data
Original description
Functional, topologically complex organic molecules are rising stars in modern materials science due to their biocompatibility, structural variability, and wealth of physico-chemical properties. Their practical applications often involve interactions with small molecular targets (e.g., gases, environmental pollutants, and drugs) via relatively weak non-covalent forces. Key to these interactions are the topological features of host materials: arrangement of functional groups, pore size, and cavity volume.Atom types and the forces connecting them in space determine molecular and material structures, defining their fundamental physical and chemical properties. These patterns comprise a universal chemical language. Numerous molecular representations exist, from strings in chemoinformatics to matrices in chemical machine learning. While these big data-oriented fingerprints generally reduce the dimensionality of atomic composition and connectivity, they do not capture the intricacies of shape and topology.
In PATTERNCHEM, several families of functional organic materials – graphenes, covalent-organic frameworks, and hyperbranched polymers – will provide a unique foundation for developing application-oriented fingerprints of their topological and non-covalent interaction features. After elucidating diverse structural descriptors of atomistic arrangement, substitution patterns, and two- and three-dimensional shapes of these materials, we will establish a scheme for quantifying the propensity for non-covalent interactions and assessing host-guest complementarity. Using this scheme, chemical and physical performance indicators relevant to targeted applications (e.g., as sensors, filters, and nanocarriers) can be computed. Finally, structure-property relationships between computed performance indicators and developed descriptors will be established and implemented into predictive frameworks for functional organic materials.
Status
TERMINATEDCall topic
ERC-2021-STGUpdate Date
09-02-2023
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