Accelerating molecular materials discovery with machine-learning

Wengert, Simon; Kunkel, Christian; Margraf, Johannes; Reuter, Karsten

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Accelerating molecular materials discovery with machine-learning

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Wengert, Simon
Theory, Fritz Haber Institute, Max Planck Society;

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Kunkel, Christian
Theory, Fritz Haber Institute, Max Planck Society;

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Margraf, Johannes
Theory, Fritz Haber Institute, Max Planck Society;

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Reuter, Karsten
Theory, Fritz Haber Institute, Max Planck Society;

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引用

Wengert, S., Kunkel, C., Margraf, J., & Reuter, K. (2021). Accelerating molecular materials discovery with machine-learning. In High-Performance Computing and Data Science in the Max Planck Society (pp. 40-41). Garching: Max Planck Computing and Data Facility. Retrieved from https://www.mpcdf.mpg.de/MPCDF_Brochure_2021.

引用: https://hdl.handle.net/21.11116/0000-0009-24D6-D

要旨

Computational molecular design is posed to accelerate the discovery of new organic materials. A brute-force screening approach is ineffective to this end, however, due to the sheer size of chemical space and the non-trivial relation between device performance and molecular proper-ties. We tackle this challenge by combining physics and machine learning.