MyJournals Home  

RSS Feeds[ASAP] Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals (Journal of Chemical Information and Modeling)

 
 

6 april 2020 21:00:19

 
[ASAP] Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals (Journal of Chemical Information and Modeling)
 


Journal of Chemical Information and ModelingDOI: 10.1021/acs.jcim.0c00003


 
20 viewsCategory: Chemistry
 
[ASAP] Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks (Journal of Chemical Information and Modeling)
[ASAP] Best Practices in Utilization of 2D-NMR Spectral Data as the Input for Chemometric Analysis in Biopharmaceutical Applications (Journal of Chemical Information and Modeling)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Chemistry


Copyright © 2008 - 2020 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten