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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.