Next Article in Journal
Void Properties in Dense Bed of Cold-Flow Fluid Catalytic Cracking Regenerator
Next Article in Special Issue
Modeling and Optimal Design of Absorbent Enhanced Ammonia Synthesis
Previous Article in Journal
Investigation of the Porosity Distribution, Permeability, and Mechanical Performance of Pervious Concretes
Previous Article in Special Issue
Optimal Multiscale Capacity Planning in Seawater Desalination Systems
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessFeature PaperArticle
Processes 2018, 6(7), 79;

Solving Materials’ Small Data Problem with Dynamic Experimental Databases

School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Author to whom correspondence should be addressed.
Received: 7 June 2018 / Revised: 24 June 2018 / Accepted: 25 June 2018 / Published: 27 June 2018
Full-Text   |   PDF [1504 KB, uploaded 27 June 2018]   |  


Materials processing is challenging because the final structure and properties often depend on the process conditions as well as the composition. Past research reported in the archival literature provides a valuable source of information for designing a process to optimize material properties. Typically, the issue is not having too much data (i.e., big data), but rather having a limited amount of data that is sparse, relative to a large number of design variables. The full utilization of this information via a structured database can be challenging, because of inconsistent and incorrect reporting of information. Here, we present a classification approach specifically tailored to the task of identifying a promising design region from a literature database. This design region includes all high performing points, as well as some points having poor performance, for the purpose of focusing future experiments. The classification method is demonstrated on two case studies in polymeric materials, namely: poly(3-hexylthiophene) for flexible electronic devices and polypropylene–talc composite materials for structural applications. View Full-Text
Keywords: materials; processing; polymers; database; classification; informatics materials; processing; polymers; database; classification; informatics

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

Printed Edition Available!
A printed edition of this Special Issue is available here.

Share & Cite This Article

MDPI and ACS Style

McBride, M.; Persson, N.; Reichmanis, E.; Grover, M.A. Solving Materials’ Small Data Problem with Dynamic Experimental Databases. Processes 2018, 6, 79.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Processes EISSN 2227-9717 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top