| EPSRC Reference: |
EP/V026887/1 |
| Title: |
Digital navigation of chemical space for function |
| Principal Investigator: |
Rosseinsky, Professor M |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Chemistry |
| Organisation: |
University of Liverpool |
| Scheme: |
Programme Grants |
| Starts: |
01 June 2021 |
Ends: |
31 May 2026 |
Value (£): |
8,699,373
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Catalysis & Applied Catalysis |
| Condensed Matter Physics |
Electrochemical Science & Eng. |
| Materials Characterisation |
Materials Synthesis & Growth |
| Robotics & Autonomy |
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| Panel History: |
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Summary on Grant Application Form |
Materials both enable the technologies we rely on today and drive advances in scientific understanding. The new scientific phenomena produced by novel materials (for example, lithium transition metal oxides) enable the creation of technologies (electric vehicles), emphasising the connection between the capability to create new materials and economic prosperity. New materials offer a route to clean growth that is essential for the future of society in the face of climate change and resource scarcity.
To harness the power of functional materials for a sustainable future, we must improve our ability to identify them. This is a daunting task, because materials are assembled from the vast and largely unknown coupled chemical and structural spaces. As a result, we are forced to work mostly by analogy with known materials to identify new ones. This necessarily incremental approach restricts the diversity of outcome from both scientific and technological perspectives. We need to be able to design materials beyond this "paradigm of analogues" if we are to exploit their potential to tackle societal challenges.
This project will transform our ability to access functional materials with unprecedented chemical and structural diversity by fusing physical and computer science. We will develop a digital discovery platform that will advance the frontier of knowledge by creating new materials classes with novel structure and bonding and tackle key application challenges, thus focussing the developed capability on well-defined targets of scientific novelty and application performance. The discovery platform will be shaped by the need to identify new materials and by the performance needed in applications. This performance is both enabled by and creates the need for the new materials classes, emphasising the interdependent nature of the project strands.
We will strengthen cutting-edge physical science (PS) capability and thinking by exploiting the extensive synergies with computer science (CS), to boost the ability of the physical scientist to navigate the space of possible materials. Computers can assimilate large databases and handle multivariate complexity in a complementary way to human experts, so we will develop models that fuse the knowledge and needs from PS with the insights from CS on how to balance precision and efficiency in the quest for promising regions in chemical space. The development of mixed techniques that use explainable symbolic AI-based automated reasoning and model construction approaches coupled with machine learning is just one example that illustrates how this opportunity goes far beyond interpolative machine learning, itself valuable as a baseline evaluation of our current knowledge.
By working collaboratively across the CS/PS interface, we can digitally explore the unknown space, informed and guided by PS expertise, to transform our ability to harvest disruptive functional materials. Only testing against the hard constraints of PS novelty and functional value will drive the discovery platform to the level needed to deliver this aim. As we are navigating uncharted space, the tools and models that we develop will be compass-like guides, rather than satellite navigation-like directors, for the expert PS team. The magnitude of the opportunity to transform materials discovery produces intense international competition with significant investments at pace from industry (e.g., Toyota Research Institute $1bn) and government (e.g., DoE $27m; a new centre at NIMS, Japan, both in 2019). Our transformative vision exploits recent UK advances in autonomous robotic researchers and artificial intelligence-guided identification of outperforming functional materials that are not based on analogues. The scale and flexibility of this PG will ensure the UK is at the forefront of this vital area.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.liv.ac.uk |