The UK’s research base for advanced materials reached a milestone in September with the official opening of the £105m Henry Royce Institute Hub Building at The University of Manchester.
Hosting £45m of new state-of-the-art equipment, the Hub Building represents the flagship of the Henry Royce Institute and is expected to play a pivotal role in supporting and driving innovation across the technology landscape.
In particular, the Henry Royce Institute - also the World Economic Forum - has drawn attention to the need for faster development of advanced materials. Decades-plus development cycles are not compatible with the time we have left to reach net-zero carbon emissions before it’s too late, for example.
To meet this need, digital technology solutions have emerged to enable faster advanced materials innovation. These include the development of artificial intelligence (AI) systems to design and/or screen for promising next-generation materials.
A diverse range of technologies stand to benefit from such developments, including clean tech (e.g., batteries, carbon capture and storage, fuel cells, solar energy conversion and thermoelectrics), displays, drug discovery and vaccine development.
As one example, an international collaboration of researchers recently used active machine learning with Bayesian neural network (BNN) models to robustly predict the bandgaps of over 2 million 2D-material heterostructures (paper accessible at this link). Here, an active machine learning model was built to identify simulated 2D-heterostructures for which the BNN models made poor predictions (large deviations) of bandgap values. These simulated bilayers were subsequently fed back to the next iteration of BNN model training, thereby accelerating model convergence towards improved predictions (lower deviations).
In providing robust predictions, the researchers’ work facilitates the experimental validation of 2D-material heterostructures whose bandgaps are ideal for a particular application, such as photovoltaics.
Elsewhere, IBM and the University of Liverpool have developed an algorithm that focusses resources on simulations worth running (see also their paper in Science Advances). When the algorithm was applied to the discovery of molecular crystals for methane capture, on top of the discoveries themselves, more than 500,000 central processing unit hours and the associated energy consumption were saved compared from the original protocol.
PROTECTING AI AND ADVANCED MATERIALS
The pace of innovation at the interface between AI and advanced materials is unlikely to slow soon: patenting AI has seen a striking increase since 2013; and the drive for advanced materials that are cleaner, greener and optimised for a target purpose continues unabated.
For academics and businesses working at this interface, IP rights are key to protecting innovation from competitors, obtaining exclusivity in commercial markets and securing investment for future work.
- What IP rights are available for my innovation?
- Can I patent an AI system and its outputs, digital and real?
- Who owns the outputs of an AI system?
These are just some of the questions pertinent to protecting innovations in the crossover between advanced materials and digital technology, which our specialist advanced materials team is well-placed to handle.
Over subsequent articles we’ll take a closer look at each question in the context of advanced materials, to help readers appreciate IP issues relevant to this rapidly evolving area.
To find out more about how IP can help you create commercial value from your advanced materials innovations, feel free to contact us at email@example.com.