++ All booklets now selling at 70% discount and fully available for free through the KDP unlimited circuit. Get them while it lasts!
Tailored content can be provided upon request. Submit your technical specification through the contact form in the About page at https://www.tenproblems.com/about/ and get your quote.
Literature Review: Informatics problem for Materials
This “Ten Problems for Materials in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.
Without further ado, these are the ten problems that we are going to introduce in this booklet:
Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.
The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.
THE PROBLEM — Materials informatics and data-driven materials science are umbrella terms for the scientific practice of systematically extracting knowledge from data produced in materials science. The data analytic techniques employed by the materials community are broad and diverse, from material design and discovery to micrograph analysis, mechanistic modelling and Bayesian inference.
CASE STUDIES — … buy this booklet from Amazon …
CONCLUSIONS — Materials informatics differs from traditional scientific approaches in materials research by the volume of processed data and the more automated way information is extracted. In addition to materials design and discovery, faster computational models, the development of autonomous and intelligent “robot researchers,” and the automatic discovery of physical models are pursued through materials informatics. Several novel applications of data science covering micrograph analysis, mechanistic modeling, and Bayesian inference are also being explored. In recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. In materials informatics, imbalanced data, standard methods for assessing quality of machine learning models break down and lead to misleading conclusions. In materials informatics, imbalanced data, standard methods for assessing quality of machine learning models break down and lead to misleading conclusions. Inverse design, which outputs materials with pre-defined target properties, has emerged as a significant materials informatics platform by leveraging hidden knowledge obtained from materials data. Modern quantum ab-initio approaches and powerful supercomputers have made it possible to directly calculate the thermophysical properties of substances. In the absence of large datasets or data that is not fully representative, domain knowledge still offers advantages in predictive ability. Many of the limitations and challenges for structural materials mirror those of the broader materials informatics field. Research shows that the materials informatics method are useful for designing thermal functional materials.
TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …