 As a researcher, you're always looking for new innovation. We're building new tools to accelerate how scientists can discover new materials. Everything we are doing is to make the discovery process more efficient, faster. We need new technologies for carbon capture. Food for the entire population. Mid-rising global demand for clean energy. Better and more sustainable devices. New drugs for an emerging pandemic. In five years. Five years. At IBM Research, we're using AI machine learning technologies to help us accelerate the way that we discover and learn about new materials and try and reduce the amount of CO2 that we're actually releasing into the atmosphere. There's a huge opportunity to capture the amount of carbon dioxide that's coming out of power plant. Flu stacks, we're looking at sorbent materials, which are usually liquids where you have the flue gas is actually bubbling through it and it's absorbing the CO2. And then we're also looking at membranes, which actually can filter it out. Carbon dioxide, once it's been captured, has a couple different destinations. We can use it to create plastics or other polymers that have a lot of value. One of the goals for the future of humanity is producing food for the entire population. Fertilizers play the very important role in the agricultural cycle. This production of fertilizer is consuming approximately two to three percent of the total energy on the planet. It's not sustainable. We got inspired by the same processes that bacteria are using with the use of novel materials. We are going to be environmentally less impactful producing fertilizers. What we are doing here is eliminating heavy metals from our battery chemistry and make battery more sustainable. The current-to-mion batteries, they use cobalt and nickel, causing some resourcing concerns with the energy-intensive mining. We chose a more sustainable and safer material set and those materials gave a pretty promising performance in fast-charging capability and higher energy density and less play mobility as well. Better batteries will limit rising global demand for clean energy and electric transportation. Everything is a computing platform these days, not just our phones, but our watches, our cars. As computer chips become ubiquitous, we really need to make sure all the materials that are used are sustainable as possible. We are trying to use advanced simulation, including quantum computing, AI, deep search, and advanced automation to help us develop better and more sustainable materials that are used in the production of computer chips. It's quite expensive to bring a new drug to market and the timeline is 10 years or more if we first look at drugs that are already approved. It's a more effective way to use our research dollars. So how would we find new drugs for an emerging pandemic like the COVID-19 crisis? The AI looks for patients who have pairs of diseases where a drug that they're taking for one disease actually turns out to be beneficial for the second disease. We think that technology will let us find drugs that can potentially address disease really quickly. We designed an approach to accelerate the entire material discovery process. It reduces the amount of time, it reduces the amount of resources. But we are changing the way we'll do discovery and then revolutionizing the world around us.