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Boeing is moving ahead with integrating chemistry and materials modelling into the product life cycle
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.
The European Multi-scale Modelling Cluster is going to hold a workshop on Interoperability in Multiscale Modeling of Nano-enabled Materials 28th – 29th May 2015 at University of Jyväskylä, Finland. The workshop program has just been posted, and I look forward to exciting, wide-ranging and in-depth discussion about the way forward on topics such as materials modelling metadata, repositories, a harmonised approach to integration and multiscale modelling platform development and more… Hope to seen many folks there.
The evidence for economic impact of molecular modelling of chemicals and materials is investigated, including the mechanisms by which impact is achieved and how it is measured.
Broadly following a model of transmission from the research base via industry to the consumer, the impact of modelling can be traced from (a) the authors of theories and models via (b) the users of modelling in science and engineering to (c) the research and development staff that utilise the information in the development of new products that benefit society at large.
The question is addressed to what extent molecular modelling is accepted as a mainstream tool that is useful, practical and accessible. A number of technology trends have contributed to increased applicability and acceptance in recent years, including
- Much increased capabilities of hardware and software.
- A convergence of actual technology scales with the scales that can be simulated by molecular modelling as a result of nanotechnology.
- Improved know-how and a focus in industry on cases where molecular simulation works well.
The acceptance level still varies depending on method and application area, with quantum chemistry methods having the highest level of acceptance, and fields with a strong overlap of requirements and method capabilities such as electronics and catalysis reporting strong impact anecdotally and as measured by the size of the modelling community and the number of patents. The picture is somewhat more mixed in areas such as polymers and chemical engineering that rely more heavily on classical and mesoscale simulation methods.
A quantitative approach is attempted by considering available evidence of impact and transmission throughout the expanding circles of influence from the model author to the end product consumer. As indicators of the research base and its ability to transfer knowledge, data about the number of publications, their growth and impact relative to other fields are discussed. Patents and the communities of users and interested ‘consumers’ of modelling results, as well as the size and growth of the software industry provide evidence for transmission of impact further into industry and product development. The return on investment due to industrial R&D process improvements is a measure of the contribution to value creation and justifies determining the macroeconomic impact of modelling as a proportion of the impact of related disciplines such as chemistry and high performance computing. Finally the integration of molecular modelling with workflows for engineered and formulated products provides a direct link to the end consumer.
Key evidence gathered in these areas includes:
- The number of publications in modelling and simulation has been growing more strongly than the science average and has a citation impact considerably above the average.
- There is preliminary evidence for a strong rise in the number of patents, also as a proportion of the number of patents within the respective fields.
- The number of people involved with modelling has been growing strongly for more than a decade. A large user community has developed which is different from the original developer community, and there are more people in managerial and director positions with a background in modelling.
- The software industry has emerged from a ‘hype cycle’ into a phase of sustained growth.
- There is solid evidence for R&D process improvements that can be achieved by using modelling, with a return of investment in the range of 3:1 to 9:1.
- The macroeconomic impact has been estimated on the basis of data for the contribution of chemistry research to the UK economy. The preliminary figures suggest a value add equivalent to 1% of GDP.
- The integration with engineering workflows shows that molecular modelling forms a small but very important part of workflows that have produced very considerable returns on investment.
- E-infrastructures such as high-throughput modelling, materials informatics systems and high performance computing act as multipliers of impact. Molecular modelling is estimated to account for about 6% of the impact generated from high performance computing.
Finally, a number of existing barriers to impact are discussed including deficiencies in some of the methods, software interoperability, usability and integration issues, the need for databases and informatics tools as well as further education and training. These issues notwithstanding, this review found strong and even quantifiable evidence for the impact of modelling from the research base to economic benefits.
We acknowledge financial support from the University of Cambridge in the production of this report.
The atomistic modelling field has grown substantially over the last 10 years, and reached a level of maturity which makes a more routine type of application and integration into engineering and product design a viable option. At the same time, product design has reached scales that are close to atomistic, and also involves exploring an ever larger space of potential new materials across the element table.
Here is some evidence:
The growth of the simulation field was demonstrated very nicely by a recent study based on publications in the ab initio field by the Psi-k network. It shows a strong increase in the number of (unique) people publishing papers based on ab initio methods from about 3000 in 1991 to about 20,000 in 2009, with particularly strong growth in East Asia. If one adds people who use other techniques such as molecular dynamics, and researchers in industry that don’t publish their work, it should be safe to assume that there are more than 30,000 users of some sort of atomistic technique.
This level of growth is also linked to the robustness of the codes and the speed of standard hardware. These together with the experience that has been gained regarding the types properties that can be calculated at a certain level of accuracy have increased the impact of atomistic simulation in many industrial applications.
Also, atomistic techniques support the combinatorial exploration of the large materials phase space. For example, the iCatDesign project in the UK explored alloys for fuel cell catalysts, considering both the combination of different elements as well as structural aspects. The online library of binary alloys from the Energy Materials Lab at Duke is an example of structure calculations that aid in the discovery and development of new materials. Considering ternary alloys are becoming more important in meeting complex requirements in high performance applications such as aerospace and energy generation, and the fact that only about 5% of ternaries are known, such modelling approaches will become even more relevant in new materials design. Also, in other areas such as polymer and composite design, early adopters are demonstrating the usefulness of integration, for example Boeing reported that they “integrated molecular simulations into the materials design process” and their work “demonstrates that the future of aerospace materials development includes simulation tools”.
Despite the growing importance and opportunity of a stronger integration of atomistic methods into engineering design, this is still an area in its infancy, but promoted strongly as part of a wider agenda such as Integrated Computational Materials Engineering (ICME). One of the key questions I am interested in is how the integration is actually achieved. For example, will integration of the modelling methods themselves be required, as in multiscale methods?
While multiscale methods are important for some applications, their significance for integration may be overrated, as was also concluded by the report on ICME report. Rather, the focus needs to be on a more detailed analysis of design workflows, and their intersection with the information that can be determined well at the atomistic scale.
A design workflow typically includes a number of selection stages, at which decisions are made regarding materials and processes. These will be informed by available data from a number of sources and should include atomistic modelling where appropriate. This type of approach has been reported for example by Massimo Noro from Unilever, who talks about selection criteria as “emerging physico-chemical criteria we can evaluate in practice that help us select ingredients”. Also Oreste Todini from Procter & Gamble promotes the use of modelling in the decision process to come up with lead options for new formulations.
So there is evidence of an integrated design approach from early adopters such as Boeing, Unilever and Procter & Gamble. In order to establish integration more widely, engineering and science communities need to collaborate more closely. The atomistic simulations community needs to improve the way in which best practices are established, shared and linked with engineering workflows. Informatics frameworks are being established, for example with the integration of Materials Studio in Accelrys’ Pipeline Pilot platform, and projects such as iCatDesign and MosGrid. However, integration into engineering rather than chemistry platforms may be what is required.