Jonathan Bean is the CEO & Co-Founder of Materials Nexus. With a background in both the theoretical and practical engineering sides of material science, Jonathan was quick to identify the opportunity for a new material modelling platform. Whilst a researcher at University of Cambridge he founded Materials Nexus to accelerate the uptake of new materials to address the climate crisis.
Jonathan’s PhD research at 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 a company using AI to make superior materials faster than ever before.
Can you share the story behind the founding of Materials Nexus? What inspired the creation of the company and its focus on AI-driven materials discovery?
Ultimately, the limit of what can be built is the materials used to build it; that was my motivation to study materials science. During my time at University of Cambridge, working with my co-founder Robert Forrest, the desire to make our research go faster inspired our pivot towards the development of machine learning algorithms. This became the foundation of Materials Nexus’ technology.
It was clear that this research could have a positive impact in the world and its adoption needed to be accelerated. In the same way, the performance of products is limited by materials, so is our progress towards net-zero. This is what inspired us to found the business.
A driving force for us as a company is to improve 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 both sustainability and performance.
Can you explain how AI is transforming the process of materials discovery, particularly in the context of Materials Nexus?
In the same way AI impacted the drug discovery process, it is also fundamentally changing materials discovery; transforming what is typically a trial-and-error-based approach to an intent-based design process. But unlike pharmaceutical research, there is the added complexity and a wider search space across the entire periodic table. At Materials Nexus, we’re looking at the entire length-scale, from quantum level to bulk – this means that we are not only leveraging quantum mechanics for composition prediction but also modelling processing and synthesis techniques. This allows us to not only identify, but also physically produce high-performance materials accurately, in a matter of months rather than decades, significantly speeding up the R&D process.
What are the key benefits of using AI over traditional trial-and-error methods in developing new materials?
Using AI for materials discovery offers several benefits: 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 process cost-effective and less wasteful, as it minimizes the need for expensive and resource-intensive experiments. This also means processes that usually take days in a lab could be done in hours on our platform.
This ultimately unlocks a new set of opportunities with targeted material “design” vs. discovery. It is possible to incorporate any data set or material parameter, such as CO2 emissions, cost, or weight, and search 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 material production?
Leveraging AI and machine learning unlocks a vast new set of material opportunities through the discovery phase. At the production level, the impact of this is two-fold; first is the elemental composition of the materials themselves, second is the materials’ processing conditions. AI materials discovery can either exclude specific elements that have a high environmental cost (e.g. rare earth elements) or reduce their compositional percentage. It can also be used to look at processing techniques (e.g. the temperature, pressure or even purity of ore) required to make the material and identify low-energy methods. These two aspects can have a significant impact on the primary emissions of material production. However, it is important to note that environmental impact goes beyond production alone. The application of superior materials, both 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. better computer chips for AI), and less toxic in their end-of-life disposal (e.g. replacing hydrofluorocarbons).
How did Materials Nexus manage to create a rare-earth-free magnet in just three months, and what are the implications of this breakthrough?
Our platform was able to analyze over 100 million potential compositions of rare-earth free magnets all before setting foot in a lab. This meant that when 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 discovery of this singular material and signals the transformation of centuries-old material design processes. As our platform becomes more developed and intelligent we will be able to predict compositions even more rapidly and across multiple material areas. With 10100 compositions of elements on the periodic table, the possibilities are endless.
Can AI potentially replace rare earth metals in other applications beyond magnets?
AI powered material discovery has the potential to identify and develop alternative materials for a vast range of applications beyond magnets. In this instance the aim was to find an alternative magnet composition that removed rare-earth elements, but our machine learning search algorithms are built to be applied to any material class. This means that we are building a universal materials design platform.
At present, our platform capabilities are focused on alloys and ceramics, with a particular focus on functional materials for applications in high-impact green-technologies such as electric motors, semi-conductors, super-conductors, and green hydrogen, to name a few.
How does the collaboration between Materials Nexus, the Henry Royce Institute, and the University of Sheffield enhance the development of new materials?
Our collaborations with key strategic partners across the UK’s innovation ecosystem, such as 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 accelerate the synthesis and testing of our predictions.
What other sectors could benefit from AI-driven materials discovery, and how?
AI-driven materials discovery can impact every material class. At Materials Nexus we focus on materials that are considered some of the most difficult, and expensive, to progress and improve, as they stand to make the biggest positive impact. Every industry will be affected: energy, aviation, supercomputing, transport, to name a few. For example, in the energy sector, AI can help develop more efficient and sustainable materials for batteries and solar cells. In supercomputing, it can lead to the creation of new 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 almost all industries.
What future advancements in AI for materials science can we expect to see, and how will they impact various industries?
Our work will continue to push the boundaries of what is possible and we’re dedicated to breaking those barriers. Superior materials mean superior innovation to meet the demands of tomorrow’s challenges. The future is only limited by our imagination.
Thank you for the great interview, readers who wish to learn more should visit Materials Nexus.
