Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships-Reference-Cited by-同舟云学术

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Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships

Published:2017-11-15 Issue:46 Volume:121 Page:8939-8954
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Janet Jon Paul¹^ORCID,Kulik Heather J.¹^ORCID

Affiliation:

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

Funder

Massachusetts Institute of Technology

Division of Electrical, Communications and Cyber Systems

Division of Chemical, Bioengineering, Environmental, and Transport Systems

Burroughs Wellcome Fund

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.7b08750

Reference114 articles.

1. The high-throughput highway to computational materials design

2. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution

3. Towards the computational design of solid catalysts

4. Accelerated DFT-Based Design of Materials for Ammonia Storage

5. Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

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