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Title: Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Authors:
ORCiD logo [1] ;  [1] ; ORCiD logo [1]
  1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
Publication Date:
Grant/Contract Number:
SC0018096
Type:
Published Article
Journal Name:
Journal of Physical Chemistry Letters
Additional Journal Information:
Journal Name: Journal of Physical Chemistry Letters Journal Volume: 9 Journal Issue: 5; Journal ID: ISSN 1948-7185
Publisher:
American Chemical Society
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
Language:
English
OSTI Identifier:
1421993

Janet, Jon Paul, Chan, Lydia, and Kulik, Heather J. Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network. United States: N. p., Web. doi:10.1021/acs.jpclett.8b00170.
Janet, Jon Paul, Chan, Lydia, & Kulik, Heather J. Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network. United States. doi:10.1021/acs.jpclett.8b00170.
Janet, Jon Paul, Chan, Lydia, and Kulik, Heather J. 2018. "Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network". United States. doi:10.1021/acs.jpclett.8b00170.
@article{osti_1421993,
title = {Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network},
author = {Janet, Jon Paul and Chan, Lydia and Kulik, Heather J.},
abstractNote = {},
doi = {10.1021/acs.jpclett.8b00170},
journal = {Journal of Physical Chemistry Letters},
number = 5,
volume = 9,
place = {United States},
year = {2018},
month = {2}
}