Predicting formulations performance via molecularly-enhanced machine learning

The formulation of specialty chemicals into consumer and commercial products is perceived as an art, rather than a reproducible science, because multiple phenomena compete with each other and affect the final product performance, depending on the situation. The consequences are significant. For example, when a new perfume is developed, its integration with an existing soap requires many trial-and-error experimental campaigns before an acceptable product is achieved. Can the integration of molecular modelling into machine learning revolutionise this situation?

The industrial partner working on this project is a world-leader in the development of new specialty chemicals; the UCL Molecular Science and Engineering Team, is a world-leader in the implementation of multi-scale simulation approaches targeted to understand structure-function-performance relationships for systems containing surface-active compounds. Combining these two synergistic ranges of expertise, this project will derive a proof-of-principle framework in which machine learning is used to correlate system composition to experimental performance, and multi-scale simulations yield fundamental understanding of the mechanisms responsible for the observations.

The production and processing of materials accounts for 15% of UK GDP and generates exports valued at £50bn annually, with UK materials related industries having a turnover of £197bn/year. It is, therefore, clear that the success of the UK economy is linked to the success of high value materials manufacturing, spanning a broad range of industrial sectors. In order to remain competitive and innovate in these sectors it is necessary to understand fundamental properties and critical processes at a range of length scales and dynamically and link these to the materials' performance. It is in this underpinning space that the CDT-ACM fits.

The impact of the CDT will be wide reaching, encompassing all organisations who research, manufacture or use advanced materials in sectors ranging from energy and transport to healthcare and the environment. Industry will benefit from the supply of highly skilled research scientists and engineers with the training necessary to advance materials development in all of these crucial areas. UK and international research facilities (Diamond, ISIS, ILL etc.) will benefit greatly from the supply of trained researchers who have both in-depth knowledge of advanced characterisation techniques and a broad understanding of materials and their properties. UK academia will benefit from a pipeline of researchers trained in state-of the art techniques in world leading research groups, who will be in prime positions to win prestigious fellowships and lectureships. From a broader perspective, society in general will benefit from the range of planned outreach activities, such as the Mary Rose Trust, the Royal Society Summer Exhibition and visits to schools. These activities will both inform the general public and inspire the next generation of scientists.

The cohort based training offered by the CDT-ACM will provide the next generation of research scientists and engineers who will pioneer new research techniques, design new multi-instrument workflows and advance our knowledge in diverse fields. We will produce 70 highly qualified and skilled researchers who will support the development of new technologies, in for instance the field of electric vehicles, an area of direct relevance to the UK industrial impact strategy.
In summary, the CDT will address a skills gap that has arisen through the rapid development of new characterisation techniques; therefore, it will have a positive impact on industry, research facilities and academia and, consequently, wider society by consolidating and strengthening UK leadership in this field.