Informatics derived materials databases for multifunctional properties.

Sci Technol Adv Mater

Institute for Combinatorial Discovery and Department of Materials Science and Engineering, Iowa State University, Ames, IA 50011, USA.

Published: February 2015

In this review, we provide an overview of the development of quantitative structure-property relationships incorporating the impact of data uncertainty from small, limited knowledge data sets from which we rapidly develop new and larger databases. Unlike traditional database development, this informatics based approach is concurrent with the identification and discovery of the key metrics controlling structure-property relationships; and even more importantly we are now in a position to build materials databases based on design 'intent' and not just design parameters. This permits for example to establish materials databases that can be used for targeted multifunctional properties and not just one characteristic at a time as is presently done. This review provides a summary of the computational logic of building such virtual databases and gives some examples in the field of complex inorganic solids for scintillator applications.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5036495PMC
http://dx.doi.org/10.1088/1468-6996/16/1/013501DOI Listing

Publication Analysis

Top Keywords

materials databases
12
multifunctional properties
8
structure-property relationships
8
databases
5
informatics derived
4
derived materials
4
databases multifunctional
4
properties review
4
review provide
4
provide overview
4

Similar Publications

Background: Anakinra is an interleukin-1 receptor antagonist (IL-1Ra). Since IL-1 has been shown to play a key role in the etiology of different autoinflammatory diseases, blocking its pathway has become an important therapeutic target, even in neonates.

Aims: We aimed to report our experience in using anakinra to treat specific neonatal inflammatory conditions.

View Article and Find Full Text PDF

Objectives: To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.

Materials And Methods: The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR.

View Article and Find Full Text PDF

Aim: To perform a systematic review to investigate if the use of audio distraction reduces signs of stress and anxiety in paediatric patients undergoing dental treatment.

Materials And Methods: Search was made in electronic databases (MEDLINE, Scopus, Embase, Web of Science, Scielo, BVS, Springer Link, Science Direct, Cochrane Library, and grey literature) until March 11th, 2024. The eligibility criteria were: paediatric patients under dental treatment; use of audio as a distraction method; comparison between groups with and without use of audio distraction; Clinical trials.

View Article and Find Full Text PDF

Avoiding positivity at a cost: Evidence of reward devaluation in the novel valence selection task.

J Exp Psychol Gen

January 2025

Department of Educational Psychology, College of Education and Human Development, University of Minnesota, Twin Cities.

Reward devaluation theory (RDT) posits that some depressed individuals may not only be biased toward negative material but also actively avoid positive material (i.e., devaluing reward).

View Article and Find Full Text PDF

Reports an error in "One thought too few: An adaptive rationale for punishing negligence" by Arunima Sarin and Fiery Cushman (, 2024[Apr], Vol 131[3], 812-824). In the original article, the copyright attribution was incorrectly listed, and the Creative Commons CC BY license disclaimer was incorrectly omitted from the author note. The correct copyright is "© 2024 The Author(s)," and the omitted disclaimer is present as: Open Access funding provided by University College London: This work is licensed under a Creative Commons Attribution 4.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!