11.8.22-Tokyo-Showcase-Xinterra

PATRICK TEYSSONNEYRE: Thank you, sir. Hello, good morning, everyone. Thank you for having us here. So my name is Patrick Teyssonneyre. I'm one of the co-founders and the CEO of Xinterra. And my connection to MIT is that I'm a Sloan Fellows MBA class of 2019. And some of my co-founders, one is a professor at MIT, and the other was a PhD student.

So at Xinterra, we are on a mission to fundamentally change the development of sustainable materials to meet the urgent challenges of the climate crisis. The current materials are in the process, it's very slow, expensive, and limited, and also the materials that we use in our day-to-day.

They work. They do their job. However, they are suboptimal in terms of performance, cost, and sustainability. To solve this problem, we created our platform named XDF, or Xinterra Design Factor. XDF has a unique combination of artificial intelligence systems, high throughput, experimentational tools, developed in-house, and deep material science expertise, and all of these combined to help companies to radically accelerate the development and application of new materials.

In this case here, a customer spent seven months trying to develop a thermo-insulating coating. They tried 69 different formulations. None of them worked. So they hired us to help to overcome this challenge. So the first thing is that they shared data with us first. So it's this data circled in red.

We can see that the sampling space is very biased and limited. And once we trained the artificial intelligence, so basically, it told us, look, if you want to find the solution-- so the specific prototype or profile-- you need to start exploring a different space here, the one circled in yellow. Otherwise the solution is not going to come from the original sampling space that the company was exploring. And then in less than one month we were able to find four formulations that already exceeded by far the desired performance by the customer.

In this other case, so the mission that is another company, so the mission given to us, was so this company, they used 15 ingredients to produce a specialty chemical. They wanted us to recombine these 15 ingredients. And we were free to remove ingredients from this formulation, in order to keep exactly the same performance, the same properties, however, reducing the cost in 5%. This is a very commoditized industry. So 5% would mean a lot of money.

And just to have a reference, 10% will be something completely unimaginable to this company. And after a little less than one month, we were able to recombine these ingredients and reduce the cost in 23%, which was mind-blowing for this customer. Basically what the machine learning recommended after being trained by the data was to remove three ingredients. Two of the ingredients they were the most expensive, and one of the ingredients was manufactured only by two companies in the world and very hard to source. And that's why the cost was high.

And the third ingredient, it was very bad for the environment. So we removed these ingredients. So after less than one month, we were able to come up with formulations and brought us, that performed exactly the same, were 23% less expensive, and much more sustainable.

So we are using our platform that combines the artificial intelligence and high throughput experimentation that I mentioned, also to create a portfolio, our own portfolio of materials IP. And this materials IP will be licensed in the near future. And we are starting to find materials that can capture CO2 from air. This is a representation of an environmental chamber that we developed in-house that can screen 50 different samples per day.

So this is 50 times larger than the status quo, which is screening one sample per day. So using these tools, we are creating a massive database on carbon capture absorption from many different existing commercial materials for now. And we will use this database to train our artificial intelligence, that very soon will start recommending what are the new materials, the next generation of materials, that could be developed and that are much better performing in terms of cost as well, and also this CO2 capture.

And the idea is to integrate these materials into several applications, including textiles and paints among others, because we want to empower every human being walking the surface of our planet to become a carbon capture agent. And we believe, so if we use in domestic applications, or close, we believe that we can make this vision become a reality. These are just examples of the data, the curves that we are generating, the data that we are collecting, and the parameters, the influence that we are understanding, and some models that we are already building.

In terms of business model, so we provide service to companies. The two first examples that I mentioned, they are service providing. This service could be very specific for a project with a very specific goal defined. Or for those companies that they want more autonomy and they want to access our platform on a more regular basis, we can do tailored AI models for them to access. And that we have a good track record here, we are already supporting companies from startups to a $20 billion US sales, end sales, multinational company.

And as I mentioned, we are using our platform to create our portfolio of materials IP, so ISIS, in the near future, starting by materials that can capture CO2 from air. Why Japan? So in terms of environment, this is a country, a culture that recognizes the urgency to combat climate change. Therefore, the market and the companies, they have urgency to launch sustainable materials.

Also digital, so the companies are very advanced on the digitization, which matches with our offer. And in terms of the culture and geography, we are based in Singapore. So basically we are almost in the same time zone. It's very, very easy to reach Japan, and also because it's a culture that technology and knowledge-centric which matches also with our offer.

In terms of collaboration needs, on the service provider, we are looking for new customers in industries like chemicals, polymers, paints, and coatings, and pulp and paper, and personal care as well. And for the CO2 capture initiative, we are looking for early-stage partners to go develop, scale up, manufacture, and commercialize the materials that we are designing and developing. And these are the priority industries, oil and gas, chemicals, textiles, paints and coatings, and appliances. Thank you very much.