Fuelled by the need for a more rational approach to materials design and by the increasing availability of massively parallel computers, computational materials science is a field in rapid expansion. The variety of time and length scales relevant to materials science means that most textbooks and researchers in the field specialise in a particular simulation scale. But this book attempts to summarise the basics and the current state of the whole field within a single, and not very lengthy, volume. It does so very successfully.
The book is divided into four parts plus appendices.
Part one introduces some general concepts in materials modelling, including random processes and simulation lattices. Modelling at the atomic scale is discussed in part two, where popular techniques like density functional theory and molecular dynamics are presented with elegance and precision. The third part, on simulation methods at the mesoscale, offers particularly instructive discussions of the kinetic Monte Carlo method and of the phase-field approach.
Finally, the novel and rapidly-growing area of materials informatics is discussed in part four, together with other concepts in simulation-assisted materials design. Obviously a book with this breadth and length cannot afford to go deep into any of the topics. Nonetheless, the author manages to present well-rounded introductions to each subject, striking a careful balance between maths and conceptual discussion, which is complemented with well selected examples and references to the research literature.
Thankfully, programming code is not listed within the book. Instead, a link is provided to a free online repository of exercises and code maintained by the author. At the time of writing this review, the collection of exercises was not fully available, but a note in the webpage promises that they will be soon. Prime members enjoy FREE Delivery on millions of eligible domestic and international items, in addition to exclusive access to movies, TV shows, and more. Back to top. Get to Know Us.
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They also need to be able to understand the technical risks, and then, of course, as businesses they need to understand any inherent financial risks, too. From the industrial perspective, some modelling tools are also not being used efficiently because they are quite difficult to use; or they may not be accurate enough; or the team may not be able to get answers to very specific questions in a timely manner.
This therefore comes back to the issue of the shortcomings in the models and the ways they can be filled.kick-cocoa.info/components/quwihakal/gexu-come-vedere-se.php
Atomistic Simulation of Materials Group
What is more, technical challenges also remain with regard to developing predictive models. The cost involved in incorporating materials modelling into SMEs is also often a barrier. Indeed, quite a significant investment is often required, at least in the beginning, and while larger companies are able to absorb this expense, smaller ones are not.
This cost can either be in terms of paying to attract the right experts to use the modelling techniques, or in terms of the infrastructure needed to support such experts. Often, both are required, which can be expensive. As such, before companies commit to investing in these areas they have to understand that by doing so they stand to begin making a profit from this within a certain timeframe, which is still very difficult to quantify.
This difficulty stems, at least in part, from the fact that many companies which have already made these investments do not make their benefits public knowledge. When we look at the numerical simulations being used in industry, continuum models based on structural mechanisms and computational fluid dynamics are typically being employed, and volume analysis within these models forms a part of the product lifecycle management, or computer-aided engineering process. This is now relatively mature but there is potential for further developments here too and so some data is available for this which can help inform the business decisions of others who may be considering investing in materials modelling.
This is a little different with regard to discrete modelling, where we are able to go into greater detail and explore material composition and structure. This requires other models and, typically, the models available here are not taken into count in product lifecycle management tools, meaning that there is a distinct lack of data which could be used to inform business decisions. Software development takes years before marketable software tools emerge. Within EMMC we are working to understand how academic software developments can be better supported in order avoid problems such as this and thus to help practically support developments to the market in a more timely manner.
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A part of this involves making the documents that we generate from the meetings we hold on areas such as different methods of professional software development or software licensing aimed at academic developers publicly available via our website. Industry has also highlighted what it perceives as a lack of communication as one of the reasons why some useful models are not being used to the extent that we would like to see. According to this argument, knowledge is not flowing between different modelling communities.
Indeed, in many instances we have seen that different applications sectors actually encounter similar problems, and they could therefore benefit from more synergy in the use of different models. While this may seem to paint a somewhat negative picture, however, it should be highlighted that there are also some good examples of where things are working well. However, we are now indeed beginning to see the integration of modelling into engineering and design.
Very often, small companies do lack some of the necessary skills for modelling, which is a problem.
This is something we would like to help companies achieve, and, again, we have made documents and guidelines around this subject publicly available on our website. The biggest benefit would be its ability to foster materials innovation and thus make the companies more competitive on the market. In addition, it would also enable them to gain a better understand with regard to processing, properties, and characterisation, from chemistry to application performance.
This will subsequently cut costs and time by avoiding unnecessary experiments or perhaps the usage of expensive and dangerous materials. Yes, absolutely. We have worked quite extensively on the economic impact of materials modelling with regard to return on investment with industrial players, and they presented some case studies to demonstrate how it has enabled them to have a shorter time to market as well as higher margin products.
- Introduction of Materials Modelling into Processing Simulation.
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However, these tools must have a high level of accuracy and also be application-oriented as opposed to only being material-oriented for them to be taken up by industry. Industry has informed us that it is important to collect the success stories from large companies and small companies alike and to present the measurable benefits of modelling.
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