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Literature Review: Research 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 — Approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties, in spite of workflow bottlenecks and multiscale issues. It is important to understand what errors are made in a given method and design the calculations to carefully control these errors. Advancements in temporal and spatial resolutions of microscopes also promise to expand the frontiers of understanding in materials science.
CASE STUDIES — … buy this booklet from Amazon …
CONCLUSIONS — Recently, the materials science community started to invest in machine learning methodologies to extract knowledge and insights from the accumulated data. The strength of high-throughput computing lies especially in identifying materials combining exceptional properties among the vast landscape of structures and chemistries and helping in finding the needle in the haystack. There are many reproducibility issues in materials informatics workflows that can affect the accuracy of results when using machine learning algorithms. Grain boundaries influence a wide array of physical properties in polycrystalline materials and play an important role in governing microstructural evolution under extreme environments. Material imaging is also one of the major experimental tools enabling new material designs and discoveries. High-throughput experimentation/computation approaches do not account for the practicalities of resource constraints which eventually result in bottlenecks at various stage of the workflow. Microwave microscopy is now exploited to perform quantitative measurements on semiconductor devices, photosensitive materials, ferroelectric domains and domain walls, and acoustic-wave systems. The GAP or Geometry, Architecture, Process methodology for composites provides a solution to help the right decisions to be taken during the early pre-concept phase. The clustered regularly interspaced short palindromic repeats (CRISPR) system is especially powerful to render programmable gels from traditional materials such as polyacrylamide and polyethylene glycol.
TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …