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Book Chapter

Accelerating molecular materials discovery with machine-learning

MPS-Authors
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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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Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-0009-24D6-D
Abstract
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.