Jonathan Bean is the CEO & Co-Founding father of Materials Nexus. With a background in each the theoretical and practical engineering sides of fabric science, Jonathan was quick to discover the chance for a brand new material modelling platform. Whilst a researcher at University of Cambridge he founded Materials Nexus to speed up the uptake of recent materials to deal with the climate crisis.
Jonathan’s PhD research on the University of York was on advanced modelling techniques for polycrystalline materials.
Alongside his role at Materials Nexus, Jonathan is a mentor with Global Talent Mentoring and the Leaders in Innovation Fellowships run by the Royal Academy of Engineering. He also teaches Materials Science for Engineers at Trinity College, Cambridge and is a Visiting Fellow at London South Bank University.
Materials Nexus is an organization using AI to make superior materials faster than ever before.
Are you able to share the story behind the founding of Materials Nexus? What inspired the creation of the corporate and its concentrate on AI-driven materials discovery?
Ultimately, the limit of what may be built is the materials used to construct it; that was my motivation to review materials science. During my time at University of Cambridge, working with my co-founder Robert Forrest, the need to make our research go faster inspired our pivot towards the event of machine learning algorithms. This became the inspiration of Materials Nexus’ technology.
It was clear that this research could have a positive impact on this planet and its adoption needed to be accelerated. In the identical way, the performance of products is restricted by materials, so is our progress towards net-zero. That is what inspired us to found the business.
A driving force for us as an organization is to enhance the state of the world, environmentally, geopolitically and ethically. Our goal is to revolutionize the materials industry by designing novel materials that meet the growing demands for each sustainability and performance.
Are you able to explain how AI is transforming the strategy of materials discovery, particularly within the context of Materials Nexus?
In the identical way AI impacted the drug discovery process, it is usually fundamentally changing materials discovery; transforming what is usually a trial-and-error-based approach to an intent-based design process. But unlike pharmaceutical research, there may be the added complexity and a wider search space across your complete periodic table. At Materials Nexus, we’re your complete length-scale, from quantum level to bulk – which means we should not only leveraging quantum mechanics for composition prediction but in addition modelling processing and synthesis techniques. This enables us to not only discover, but in addition physically produce high-performance materials accurately, in a matter of months fairly than a long time, significantly speeding up the R&D process.
What are the important thing advantages of using AI over traditional trial-and-error methods in developing recent materials?
Using AI for materials discovery offers several advantages: speed, cost-efficiency, and sustainability being key. Our AI-driven platform can analyze vast datasets and predict material properties accurately, all before setting foot in a lab, making the method cost-effective and fewer wasteful, because it minimizes the necessity for expensive and resource-intensive experiments. This also means processes that sometimes take days in a lab may very well be done in hours on our platform.
This ultimately unlocks a brand new set of opportunities with targeted material “design” vs. discovery. It is feasible to include any data set or material parameter, comparable to CO2 emissions, cost, or weight, and seek for compositions to match those specific needs, flipping the “discovery” process on its head.
What role do AI and machine learning play in reducing the environmental impact of fabric production?
Leveraging AI and machine learning unlocks an enormous recent set of fabric opportunities through the invention phase. On the production level, the impact of that is two-fold; first is the basic composition of the materials themselves, second is the materials’ processing conditions. AI materials discovery can either exclude specific elements which have a high environmental cost (e.g. rare earth elements) or reduce their compositional percentage. It may possibly even be used to take a look at processing techniques (e.g. the temperature, pressure and even purity of ore) required to make the fabric and discover low-energy methods. These two facets can have a major impact on the first emissions of fabric production. Nonetheless, it’s important to notice that environmental impact goes beyond production alone. The applying of superior materials, each high performance or cheaper, can have a hugely positive secondary environmental impact by making sustainable technologies more accessible (e.g. cheaper EVs), more efficient (e.g. higher computer chips for AI), and fewer toxic of their end-of-life disposal (e.g. replacing hydrofluorocarbons).
How did Materials Nexus manage to create a rare-earth-free magnet in only three months, and what are the implications of this breakthrough?
Our platform was able to research over 100 million potential compositions of rare-earth free magnets all before setting foot in a lab. This meant that after we progressed to the synthesis stage that we already had an accurate prediction of the composition and its properties.
The implications of this magnet are significant: the breakthrough goes beyond the invention of this singular material and signals the transformation of centuries-old material design processes. As our platform becomes more developed and intelligent we are going to give you the option to predict compositions much more rapidly and across multiple material areas. With 10100 compositions of elements on the periodic table, the chances are limitless.
Can AI potentially replace rare earth metals in other applications beyond magnets?
AI powered material discovery has the potential to discover and develop alternative materials for an enormous range of applications beyond magnets. On this instance the aim was to seek out an alternate magnet composition that removed rare-earth elements, but our machine learning search algorithms are built to be applied to any material class. Which means that we’re constructing a universal materials design platform.
At present, our platform capabilities are focused on alloys and ceramics, with a selected concentrate on functional materials for applications in high-impact green-technologies comparable to electric motors, semi-conductors, super-conductors, and green hydrogen, to call just a few.
How does the collaboration between Materials Nexus, the Henry Royce Institute, and the University of Sheffield enhance the event of recent materials?
Our collaborations with key strategic partners across the UK’s innovation ecosystem, comparable to the Henry Royce Institute and the University of Sheffield, provide access to world-class facilities and expertise in specialized areas of materials science. These partnerships enable us to speed up the synthesis and testing of our predictions.
What other sectors may gain advantage from AI-driven materials discovery, and the way?
AI-driven materials discovery can impact every material class. At Materials Nexus we concentrate on materials which might be considered a number of the most difficult, and expensive, to progress and improve, as they stand to make the most important positive impact. Every industry shall be affected: energy, aviation, supercomputing, transport, to call just a few. For instance, within the energy sector, AI will help develop more efficient and sustainable materials for batteries and solar cells. In supercomputing, it could possibly result in the creation of recent semi-conductor materials that enhance data storage and processing capabilities. By enabling the rapid development of high-performance materials, AI can drive innovation and sustainability across just about all industries.
What future advancements in AI for materials science can we expect to see, and the way will they impact various industries?
Our work will proceed to push the boundaries of what is feasible and we’re dedicated to breaking those barriers. Superior materials mean superior innovation to satisfy the demands of tomorrow’s challenges. The longer term is simply limited by our imagination.
