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  Accelerating molecular materials discovery with machine-learning

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.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-24D6-D Version Permalink: https://hdl.handle.net/21.11116/0000-0009-24D7-C
Genre: Book Chapter

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 Creators:
Wengert, Simon1, Author           
Kunkel, Christian1, Author           
Margraf, Johannes1, Author           
Reuter, Karsten1, Author           
Affiliations:
1Theory, Fritz Haber Institute, Max Planck Society, ou_634547              

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 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.

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Language(s): eng - English
 Dates: 20212021
 Publication Status: Issued
 Pages: 2
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: URI: https://www.mpcdf.mpg.de/MPCDF_Brochure_2021
 Degree: -

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Title: High-Performance Computing and Data Science in the Max Planck Society
Source Genre: Book
 Creator(s):
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Publ. Info: Garching : Max Planck Computing and Data Facility
Pages: 2 Volume / Issue: - Sequence Number: - Start / End Page: 40 - 41 Identifier: -