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Industry Case Studies: combining discrete and continuum modelling to address industrial R&D challenges
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.
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.
In the context of my work with the European Materials Modelling Council, I recently launched a survey to gather success stories and information about the economic impact of materials modelling. The survey aims to build on my previous report which was mainly based on a literature review.The survey is inspired by studies that have been done by IDC in the area of High Performance Computing, in particular their Innovation and ROI Awards. The results of their study show that even without absolutely complete information, a clear picture of the impact emerges as more and more cases are gathered.
Already ten organisations have taken part, and I am looking forward to the opportunity to discuss the initial findings at an international cooperation workshop on multiscale materials modelling organised by the European Commission in September.
I recently carried out a survey on behalf of the Psi-k network of the European ab initio research community and the CECAM-UK-JCMaxwell Node. The full report can be accessed here, and below is an overview.
The report explores the interactions of the academic Psi-k community with industry and is based on a semi-quantitative survey and interviews of network members. The evidence is analysed in the context of a prior report on the economic impact of molecular modelling [i] as well as of a recent study into Science-to-Business (S-2-B) collaborations [ii] in general.
Pertinent findings of the economic impact report were that the dominant electronic structure method, Density Functional Theory (DFT), is the most widely accepted ‘molecular modelling’ method and that it has become established in the electronics industry. Also of significance are the more than average growth in the number of patents which include DFT, and the growing interest in the potential of modelling in a wider circle of researchers in industry.
The S-2-B study [ii] emphasized the key role of the Principal Investigator (PI) in establishing and maintaining a satisfactory relationship, and the importance to industry of ‘soft’ objectives relative to outcomes with hard metrics.
All Psi-k board, working group and advisory group members, a total of about 120 people were invited to take part in the study, and 40 people responded, representing more than 400 scientists from 33 different institutions in 12 European countries. While it is acknowledged that this group will to some extent pre-select those with industry collaborations, the result that 90% of respondents work with industry is still significant. Main industry sectors of the collaborators are materials, electronics, automotive and aerospace and software. Density functional theory is almost always used in industry collaborations but classical and higher level theory also feature strongly.
It was noted that the Psi-k network represents some of the most widely used electronic structure codes in the world. In fact, all electronic structure codes available in the leading commercial packages originate from Europe and are used at a few hundred industrial sites worldwide.
Psi-k groups that work with industry collaborate on average with 2-3 companies, typically on a long term basis. It is interesting that small groups are just as likely to collaborate with industry as larger ones, and also with roughly the same number of companies. There is however a correlation between the number of collaborating companies and the number of alumni in industry positions, which is consistent with the observation of the S-2-B study that the role of the PI and the depth of the relationship are the dominant factors.
Considering the different forms of interactions, informal interactions dominated, followed by collaborative projects, consultancies and training. Collaborative projects were reported by 75% of respondents with on average one such project per team per year. Nearly 60% of respondents had consultancy and contract research projects, with an average of one such engagement per research team every 1-2 years. Training was least frequent but still more than 40% of respondents had training interactions in the last three years.
The main drivers for industry to collaborate are seen to be the expertise of the PI and access to new ideas and insights. As measures of success, new insights dominate followed by achieving breakthroughs in R&D. On the other hand, despite a clear ROI, cost saving is not generally the driver for collaborations. Impact was often achieved by unveiling mechanisms that could explain observations on a fundamental level and that had previously not been known or properly understood. The new insights thereby helped to overcome long standing misconceptions, leading to a completely new way of thinking and research direction. Similarly, electronic structure calculations helped to scrutinize certain concepts or aspects of engineering models. Less frequently so far seems to be the determination of input parameters for these models. However, the ability of simulations to screen a large number of systems, which would be prohibitively expensive if done experimentally, also plays an important role.
The above evidence and mechanisms of success indicate that the Psi-k network is largely in line with S-2-B collaborations in general, for example in terms of the relationships, importance of PI and the typical ‘soft’ measures of success.
On the other hand we can also see significant opportunities for further improvement. There is sincere interest as well as unmet need in industry. On the one hand, the gap between industry requirements and what can be delivered by today’s theories and simulations is widely acknowledged. On the other hand, there is plenty of evidence that important and impactful topics can be addressed with current methods. However it takes a lot of time, effort and translation skills to identify and act upon these. Despite some activities by the network to further the exchange with industrial research, there is still too little common ground in terms of interactions, interests and language to develop the personal relationships that were found to be crucial for engagements between academics and industry.
However, we see evidence of successful mechanisms that can be built upon. These include utilising multiscale modelling approaches as not only a scientific endeavour but also as an opportunity to build a bridge in terms of communication and relationships. Also, relationships with industry at the level of Ph.D. training seems to be an effective mechanisms not only to train scientists with the relevant skills and understanding but also to build long term relationships between the academic centres and industry. Similarly, centres of excellence that are by their nature set up with industry involvement provide visibility and help to build relationships, although with the proviso [ii] that the single investigator can be the critical determinant.
[i] Goldbeck, G. The economic impact of molecular modelling. Goldbeck Consulting Limited, Available via https://gerhardgoldbeck.wordpress.com/2012/07/10/the-economic-impact-of-molecular-modelling-of-chemicals-and-materials/ (2012).
[ii] Boehm, D. N. & Hogan, T. Science-to-Business collaborations: A science-to-business marketing perspective on scientific knowledge commercialization. Industrial Marketing Management 42, 564–579 (2013).
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.