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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Journal Article · · Chemical Reviews
 [1];  [1];  [2];  [2];  [2];  [2]
  1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States, Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
  2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
+ Show Author Affiliations
Not Available
Research Organization:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Organization:
National Science Foundation; USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
NA0003965; SC0012702; SC0018096; SC0019112
OSTI ID:
1808009
Alternate ID(s):
OSTI ID: 1814903
Journal Information:
Chemical Reviews, Journal Name: Chemical Reviews Journal Issue: 16 Vol. 121; ISSN 0009-2665
Publisher:
American Chemical SocietyCopyright Statement
Country of Publication:
United States
Language:
English

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Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening
Journal Article · Mon Aug 31 00:00:00 EDT 2020 · Journal of Physical Chemistry Letters · OSTI ID:1852494

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Chemical structure
Density functional theory
Ligands
Machine learning
Molecular mechanics