Following the proposal by a group of European scientists involved in materials modelling CEN (the European Committee for Standardization) has announced a new workshop on the subject “Materials modelling terminology, classification and metadata”. It is based on many years of effort led by the European Commission and the European Materials Modelling Council (EMMC), as expressed in the Review of Materials Modelling (RoMM), which will be released in its sixth edition in January 2017. The aim is to agree on a terminology and classification of materials models and organise the description of materials modelling applications based on a system referred to as MODA (Materials Modelling Data). A common terminology in materials modelling should lead to simplified and much more efficient communication and lower the barrier to utilising materials modelling. The end result is the adoption of a CEN Workshop Agreement (CWA), a best practices document for further standardisation efforts and input for the development of a future certification scheme.
In recognition of the importance of materials modelling for industrial innovation and the strength of Europe, a new Horizon 2020 project has been funded to augment and further boost the actions of the European Materials Modelling Council (EMMC). The new European Materials Modelling Council Coordination and Support Action (EMMC-CSA) includes 15 partners and is coordinated by TU Wien.
Goldbeck Consulting is part of the EMMC management team and leads Work Package 2 on Interoperability and Integration of materials modelling.
For further information, see the EMMC-CSA Press Release.
Materials modelling is used today by a range of industries to improve efficiency and achieve breakthroughs in the development of new and improved materials and processes. A set of four case studies has been developed by the European Materials Modelling Council which demonstrate how industrial R&D problems have been addressed by the integration of different types of materials models and what technical and technological benefits and business impacts were achieved as a result.
The case studies cover a diverse set of applications and industries, including chemical processing (Covestro), discovery of new functional materials (IMRA Europe), additive manufacturing of engine parts (MTU Aero Engines) and magnetic hard drive materials (Seagate):
- Identification of Solvents for Extractive Distillation
- Discovery of new thermoelectric materials
- Simulation of additive manufacturing of metallic components
- Integrated Recording Model for Heat Assisted Magnetic Recording (HAMR)
The case studies were compiled with the support of the EC Industrial Technologies Programme.
On Thursday, 10 March 2016, the European Materials Modelling community held a workshop to discuss metadata and interoperability in materials modelling. The following overview is based on an introduction provided by Adham Hashibon (Fraunhofer IWM).
The purpose of the meeting was to discuss a holistic view on materials modelling data, recognising the universal structure of all models ( Physics Equations (PE) and Material Relations (MR)). It was shown how all basic elements of materials modelling can be represented in a four chapter organisation, the so-called MODA. Such a universal structure will allow a more focused interpretation of modelling information.
The question addressed in the meeting was how to represent knowledge and not just a collection of raw data (numbers). The metadata extracted by means of these MODA are used to establish interoperability between different types of models and between models and data.
The interoperability is achieved by a fundamental open metadata schema that is based on the elements of material modelling. Starting from this fundamental scheme, means to achieve both syntactic and semantic interoperability were discussed and how these can be further extended to achieve a more global, cross domain level of interoperability. This metadata schema is capable of providing a channel to link different specific domain standards. The schema is not intended to replace existing specific standards, but is rather intended to harmoniously integrate with, and augment existing domain and implementation specific standards of data. The schema is therefore providing for new fundamental interoperability avenues.
The proposed modelling element structures and metadata schema are neutral to any implementation in specific computer programming languages or formal mark-up schemes and also not bound to any specific data file format. Nevertheless, specific examples of implementations of the specification of the schema in both simple language (MODA) and the more formal mark-up languages such as YAML and JSON were presented. Additionally, it was shown that widely endorsed HDF5 based file formats, with their associated simple hierarchical data model can implement the data schema rapidly and efficiently.
The underlying fundamental open schema is further supported by a basic syntactic layer that provides common universal basic attributes (CUBA) defining a set of internally constrained materials modelling vocabulary. The semantics used for the CUBA are further elaborated in the schema allowing machine interpretations so that translations to other domain specific syntaxes and standards are seamless. This is achieved by incorporating a semantic level augmenting the e-CUBA with a common universal data structure (e-CUDS) that provides a neutral representation of the computational metadata including elements from the user case description. In essence, the e-CUDS provide the open semantic based metadata schema and the e-CUBA provide a common language to bridge the nomenclature gap between specific domains and communities.
It was shown that the MODA together with the e-CUDS and e-CUBA allow for a representation of the computational metadata of all models, including electronic, atomistic, mesoscopic and continuum models.
The workshop concluded with a series of challenges presented from the engineering, manufacturers and software owner view points. A particular case example of delamination was posed and answered by a formal representation within the schema presented.
An article just appeared which summarizes (and includes case examples for) some important lessons for the application of computational methods in drug design. I write about it here because it includes important messages also for the materials modelling and design field. There are differences of course since materials innovation is not always about new materials design. Nevertheless some of the key points are still valid and at least worth considering. I like the ‘principle of parsimony’, and also the conclusion about the importance of good software design. Much needed in the materials field as well.
Here are some key quotes and extracts from the paper .
The value of qualitative statements. Frequently, a single new idea or a pointer in a new direction is sufficient guidance for a project team. Most project impact comes from qualitative work, from sharing an insight or a hypothesis rather than a calculated number or a priority order. The importance of this observation cannot be overrated in a field that has invested enormously in quantitative prediction methods. We believe that quantitative prediction alone is a misleading mission statement for molecular design. …
Shaping chemical space. At any given point during a project, a team’s focus is either on expanding chemical space or on narrowing it down, for different aspects of problem solving and optimization. Broadening chemical space requires methods that create new ideas within a set of constraints. ….Narrowing down chemical space can be a simple filtering process or can be based on a specific hypothesis. Within a given project context, it is important to understand whether it is required to broaden or narrow down chemical space and to choose tools and approaches accordingly. As projects progress towards candidate selection, the “amplitudes” of narrowing and broadening space typically become smaller, but the concept stays the same.
The principle of parsimony. Molecular design is a conceptual process and therefore always at risk of losing touch with reality. The scientific questions should lead to the method, and not vice versa. To achieve this, it is a helpful guiding principle to keep things as simple as possible. Choosing the simplest possible explanation and the simplest possible computational protocol leads to agility and to a better focus on the key questions at hand. …
Annotation is half the battle. … Contextual information can add value almost anywhere. A good deal of frontloading work—computational, organizational—is often required to bring data into a useful shape. Proper frontloading work can turn sophisticated queries into simple lookup processes or visualization steps. There is a significant growth potential in this area.
Staying close to experiment. One way of keeping things as simple as possible is to preferentially utilize experimental data that may support a project, wherever this is meaningful. … Rational drug design has a lot to do with clever recycling. If consistently applied, these guidelines have significant implications for the current practice of molecular design.
Let us look at some of the more problematic aspects as well. Many computational methods introduce additional parameters and thus potential sources of error that make the predictive value harder to extract. …..
What is special about molecular design is the need to build solid hypotheses and to simultaneously foster creative thinking in medicinal chemistry. If we accept this, our focus may shift from the many semi-quantitative prediction tools that we have to methods supporting this creative process. Further improvements in computational methods may then have less to do with science than with good software engineering and interface design. The tools are a just means to an end. Good science is what happens when they are appropriately employed.
 A Real-World Perspective on Molecular Design. Bernd Kuhn, Wolfgang Guba, Jérôme Hert, David W. Banner, Caterina Bissantz, Simona Maria Ceccarelli, Wolfgang Haap, Matthias Körner, Andreas Kuglstatter, Christian D. Lerner, Patrizio Mattei, Werner Neidhart, Emmanuel Pinard, Markus G. Rudolph, Tanja Schulz-Gasch, Thomas J. Woltering, and Martin Stahl
J. Med. Chem., DOI: 10.1021/acs.jmedchem.5b01875 • Publication Date (Web): 15 Feb 2016
The EMMC Roadmap for Materials Modelling is calling for a number of actions to increase the application and impact of materials modelling in industry. In its Objectives and Vision it states that “the ultimate goal is that materials modelling and simulation will become an integral part of product life cycle management in European industry” and that “in recent years, materials modelling of nano-scale phenomena, especially that based on discrete models (electronic/atomistic/mesoscopic) has developed rapidly. However, this has not yet led to the integration of these models as part of the industrial design tool chain of materials and products.” While true in general, it turns out that world leading organisations are already implementing such integration.
An example of a company that seems to be well ahead of the curve is Boeing. In fact, this blog was triggered by a panel discussion that I participated in at the Predictive Materials Modelling workshop in Cambridge early December 2015. The panel on Aerospace applications was led by Airbus who presented their elaborate work on virtual testing of aircraft frames. While the work is clearly very challenging in terms of the computational resolution in FEM models required and the issues in identifying ‘hotspots’ etc., at this stage of development the materials are already well defined and no variation in material parameters are allowed or considered any more. So what about the actual materials development and its integration into this process as outlined in the vision above?
Certainly Airbus competitor Boeing has been very active in the materials modelling field down to the chemistry level for some time, see for example Reference [[i],[ii]]. Rather than relying on the traditional supply chain dynamics, Boeing has become involved in chemistry based research in-silico, thereby taking a pro-active role in shaping its own future across all disciplines.
Three recent Boeing patents actually demonstrate the significance of the corner-stones of exploitation of materials modelling at the industrial level which are also highlighted in the EMMC roadmap (a) materials modelling has been developed to a point that it can make an impact on real industrial problems, (b) multi-scale modelling workflows are key to realising impact, (c) it is important for business efficiency and effectiveness to integrate information gained down to the chemistry level into wider information management and business decision support systems.
Testimony to (a) is Patent US20150080494 (Filing date: 4 Feb 2014) “Fiber-reinforced resin composites and methods of making the same”. It deals with the efficiency of load transfer between the fiber and the surrounding matrix at the micro-scale level, which may directly affect the overall mechanical performance of the composite at the continuum level. “The region of the matrix that may be substantially affected by the presence of fibers, sometimes referred to as the “interphase” region, is the interfacial area of the matrix directly surrounding the fiber. In composites, this interphase region may experience high shear strain due to the mismatch in elastic stiffness between the fibers and the surrounding matrix. Widely-used conventional bulk resins may not provide desirable distortional capabilities.” The patent claims superior performance of resins developed with the help of atomistic materials modelling. This performance improvement could translate into substantial efficiency in load bearing and associated lower weight of the aircraft frame.
Testimony to (b) is Patent US 08862437 (Application date: 30 Mar 2010) on “Multi-scale modeling of composite structures”. The following patent abstract is a bit hard to read but basically seems to claim that there is a controlled, deterministic relationship between composite performance and materials/chemical structure at various levels, as calculated by modelling: “A method, apparatus, and computer program product are present for creating a composite structure. A number of characteristics for a number of components for the composite structure is obtained from a simulation of the composite structure using a model of the composite structure. A number of changes in the number of characteristics needed to meet a desired level of performance for the number of characteristics is ascertained. A number of attributes for a number of composite materials used to form the number of components corresponding to the number of characteristics having the number of changes is identified. The number of attributes for the number of composite materials for the number of characteristics having the number of changes based on the desired level of performance is changed.”
Testimony to (C) is Patent WO2015060960 (Filed on 18 Sep 2014): “Product Chemical Profile System”. The abstract describes as system that is able to pull together and query all levels of information about a product down to the chemistry level: “A computer-implemented system and method for obtaining product related information obtained from a plurality of different sources that is transformed into processed product data with a plurality of levels. Callouts and contexts are identified and a product-to-chemical continuum is generated by creating callout-context pathway segments between the plurality of levels of the processed product data based on the callouts and contexts identified and a transformed query request is generated used to traverse the product-to-chemical continuum through the callout-context pathway segments that span the plurality of levels. The product information that matches the set of context search parameters is extracted from the product-to-chemical continuum. The callout context pathway segments reduce processing resources and time needed to obtain the product information.”
These patents are a clear recognition of the relevance and importance materials modelling and a more integrated approach to engineering. The question remains however how to tear down barriers preventing its wider exploitation across the whole community. That is what the EMMC Roadmap, current and forthcoming Horizon2020 actions aim to address.
[i] A. Browning, “Utilization of Molecular Simulations in Aerospace Materials: Simulation of Thermoset Resin/Graphite Interactions,” Proceedings of AIChE Fall Annual Meeting, 2009.
[ii] Knox, C. K., Andzelm, J. W., Lenhart, J. L., Browning, A. R., & Christensen, S. (2010, December). High strain rate mechanical behavior of epoxy networks from molecular dynamics simulations. In Proc of 27th army science conf, Orlando, FL, GP-09.
Here is the executive summary of a new report on the economic impact of materials modelling, co-authored with Christa Court from MRIGlobal in the framework of the European Materials Modelling Council (EMMC) and the International Materials Modelling Board (IM2B). The full text as well as survey form is available here.
At the core of the report is an industry survey conducted during 2015 that provides corroboration for the indicators of research and development (R&D) process improvements found in earlier studies and new data relevant for quantitative economic analyses.
The survey is set in the context of an outline of metrics and methodologies that can be used to quantify the economic impacts of materials modelling from a variety of perspectives including R&D and industry stakeholders and society at large. At the micro-economic level, performance indicators include financial metrics such as net present value, return on investment (ROI), and internal rate of return. Where sufficient data are available, micro-economic analyses could be extended to a more in depth cost benefit analysis. Finally, macro-economic modelling methodologies can be used to model the wider impacts of the integration of materials modelling into the production function of various industries. Since materials modelling is a potentially disruptive technology, macro-economic impact assessment will likely require dynamic simulation models, which are scenario specific and necessitate someone with a high level of both problem domain knowledge and modelling domain knowledge.
Research impact is reviewed briefly based on bibliometrics, case studies, peer review, and economic analysis  using evidence gathered for a previous report  as well as the recent UK Research Excellence Framework , which includes 15 cases involving materials modelling.
The study also investigates how materials modelling impacts the industrial R&D process and outlines the value and potential of materials modelling for industrial research and innovation, competitiveness, and profitability using examples from materials industries based on recent Integrated Computational Materials Engineering studies and a Computer-Aided Drug Design study, which demonstrated the usefulness of defining a performance metrics for a modelling function in an industrial R&D organisation.
The survey analysis was based on information provided by 29 companies covering a wide range of sizes and industry sectors and an even distribution in terms of types and scales of modelling. The qualitative benefits identified in the responses were categorised into the following Key Performance Indicators: More efficient and targeted exploration; Deeper understanding; Broader exploration; R&D strategy development; Source of property data; Trouble shooting; Performance optimisation; Intellectual property protection; Value chain benefits; Improved communication and collaboration between R&D and production; Upscaling and market introduction as well as marketing benefits.
On a quantitative level about 80% of companies reported innovation accomplishment, 60% cost savings, 35% job creation, and 30% revenue increase due to materials modelling. A wide variety of project sizes are represented, with total materials modelling investment (covering staff, software and hardware) ranging from €45K to €4M (average €1M, median €½M). Staff was the largest cost factor: the ratio of staff costs to the median cost of software and hardware, respectively, is 100/20/6. Cost savings due to the materials modelling project ranged from €100K to €50M (average €12M, median €5M). The ROI, determined by the ratio of revenue generated and investment in modelling, ranged from 2 to 1000. Removing the largest and the smallest values yields an average ROI of 8. A trend for ROI to grow more than linearly with investment in modelling was found.