Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry-Reference-Cited by-同舟云学术

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Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

Published:2018-09-24 Issue:42 Volume:57 Page:13973-13986
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Nandy Aditya¹²,Duan Chenru¹²,Janet Jon Paul¹^ORCID,Gugler Stefan¹³,Kulik Heather J.¹^ORCID

Affiliation:

1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

2. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

3. Laboratorium für Physikalische Chemie, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland

Funder

Defense Advanced Research Projects Agency

Massachusetts Institute of Technology

Office of Naval Research

U.S. Department of Energy

Division of Chemical, Bioengineering, Environmental, and Transport Systems

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.8b04015

Reference109 articles.

1. Switching of Molecular Spin States in Inorganic Complexes by Temperature, Pressure, Magnetic Field and Light: Towards Molecular Devices

2. Light-induced excited spin state trapping in a transition-metal complex: The hexa-1-propyltetrazole-iron (II) tetrafluoroborate spin-crossover system

3. Light-Induced Spin Crossover and the High-Spin→Low-Spin Relaxation

4. Reversible CO Scavenging via Adsorbate-Dependent Spin State Transitions in an Iron(II)–Triazolate Metal–Organic Framework

5. Enhanced Cooperativity in Supported Spin-Crossover Metal–Organic Frameworks

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