Kim E Jelfs
researcher
Facts
11 properties- Instance of
- human
- Date of birth
- 1950
- Occupation
- chemist
- Employer
- Imperial College London[4]
- Field of work
- computational chemistry[3]
- Educated at
- University College London[5]
- Award received
- Harrison-Meldola Memorial Prize[6]
🏷️ Also known as
en Kim E Jelfs
en Kim Jelfs
🔖 Identifiers · 13
- ISNI
- 0000000508386339
- ORCID iD
- 0000-0001-7683-7630
- VIAF cluster ID
- 7761163877690746060004
- Library of Congress Authorities ID
- no2021142139
- DBLP author ID
- 227/8661
- Dimensions author ID
- 01040303052.02
- Google Knowledge Graph ID
- /g/11ghs9llq_
- Google Scholar author ID
- r3IdtREAAAAJ
- Mastodon address
- jelfschem@fediscience.org (2022–present)
- Publons author ID
- 2789934
- Web of Science ResearcherID
- E-1802-2011
- X (Twitter) username
- JelfsChem (2014–present)
Show all 13 identifiers +1
- Yale LUX ID
- person/e24b7f68-4c68-42f8-a743-34f246109ce7
🔗 Connections
Author of 10
Deep generative design of porous organic cages <i>via</i> a variational autoencoder,
Explainable graph neural networks for organic cages,
HCGA: Highly comparative graph analysis for network phenotyping,
High‐Throughput Computational Evaluation of Low Symmetry Pd <sub>2</sub> L <sub>4</sub> Cages to Aid in System Design**,
Machine Learning for Organic Cage Property Prediction,
Mapping binary copolymer property space with neural networks,
Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors,
Molecular generation targeting desired electronic properties <i>via</i> deep generative models
+2 more
← Authored by 10
Deep generative design of porous organic cages <i>via</i> a variational autoencoder,
Explainable graph neural networks for organic cages,
HCGA: Highly comparative graph analysis for network phenotyping,
High‐Throughput Computational Evaluation of Low Symmetry Pd <sub>2</sub> L <sub>4</sub> Cages to Aid in System Design**,
Machine Learning for Organic Cage Property Prediction,
Mapping binary copolymer property space with neural networks,
Materials Precursor Score: Modeling Chemists’ Intuition for the Synthetic Accessibility of Porous Organic Cage Precursors,
Molecular generation targeting desired electronic properties <i>via</i> deep generative models
+2 more
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Summary
Kim E Jelfs was born in 1950[1]. She worked as a chemist[2].
FAQs
What did Kim E Jelfs do for work?
Kim E Jelfs worked as chemist[2].
Where did Kim E Jelfs go to school?
Kim E Jelfs was educated at University College London[5].
What awards did Kim E Jelfs receive?
Honors received include Harrison-Meldola Memorial Prize[6].