\n<\/p>\n
Ten billion. That\u2019s<\/span> how many commercially procurable molecules are available today. Start looking at them in groups of five \u2014 the typical combination used to make electrolyte materials in batteries \u2014 and it increases to 10 to the 47th power.<\/p>\n For those counting, that\u2019s a lot.<\/p>\n All of those combinations matter in the world of batteries. Find the right mixture of electrolyte materials and you can end up with a faster charging, more energy dense battery for an EV, the grid or even an electric airplane. The downside? Similar to the drug discovery process, it can take more than a decade and thousands of failures to find the right fit.<\/p>\n That\u2019s where founders of startup Aionics<\/a> say their AI tools can speed things up.<\/p>\n \u201cThe problem is there\u2019s too many candidates and not enough time,\u201d Aionics co-founder and CEO Austin Sendek told TechCrunch during the recent Up Summit event in Dallas.<\/p>\n Dr. Lenson Pellouchoud, co-founder and CTO; Dr. Austin Sendek, co-founder and CEO and Dr. Venkat Viswanathan, co-founder and chief scientist Image credits<\/strong>: Aionics<\/p>\n<\/div>\n Lithium-ion batteries contain three critical building blocks. There are two electrodes, an anode (negative) on one side and a cathode (positive) on the other. An electrolyte typically sits in the middle and acts as the courier to move ions between the electrodes when charging and discharging.<\/p>\n Aionics is focused on the electrolyte and it\u2019s using an AI toolkit to accelerate discovery and ultimately deliver better batteries. Aionics approach to catalyst discovery has also attracted investors. The Palo Alto-based startup, which was founded in 2020, has raised $3.5 million to date, including a $3.2 million seed round from investors that included UP.Partners.<\/p>\n The startup is already working with several companies, including Porsche\u2019s battery manufacturing subsidiary Cellforce. The company has also worked with energy storage firm Form Energy, Japanese materials and chemical maker Showa Denko (now Resonac) and battery tech company Cuberg.<\/p>\n This whole process starts with a company\u2019s wish list \u2014 or performance profile \u2014 for a battery. Aionics scientists, using AI-accelerated quantum mechanics, can run experiments on an existing database of billions of known molecules. This allows them to consider 10,000 candidates every second, Sendek said. That AI model learns how to predict the outcome of the next simulation and helps select the next molecule candidate. Every time it runs, more data is generated and it gets better at solving the problem.<\/p>\n Aionics has taken this a step further, in some cases, by bringing generative AI into the mix. Instead of relying on the billions of known molecules, Aionics started using this year generative AI<\/a> models trained on existing battery materials data to create or design new molecules targeted at a certain application.<\/p>\n The company is super-charging its effort by using software developed in the Accelerated Computational Electrochemical systems Discovery program at Carnegie Mellon University. Venkat Viswanathan, who was associate professor at CMU and led that program, is co-founder and chief scientist at Aionics.<\/p>\n Aionics has also started using large language models built on GPT 4 from OpenAI to help its scientists winnow down the millions of possible formulations before they even start running them through the database. This chatbot tool, which has been trained on chemistry textbooks and scientific papers selected by Aionics, isn\u2019t used for the actual discovery, but it can be used by scientists to eliminate certain molecules that wouldn\u2019t be useful in a particular application, Sendek explained.<\/p>\n Once trained with those textbooks, LLMs allow the scientist to query the model. \u201cIf you can talk to your textbook, what would you ask it?\u201d Sendek said. But he was quick to note that this isn\u2019t doing anything different than a person curating scientific papers. \u201cThis is just providing some next level interaction,\u201d he said, adding that everything is verifiable by pointing back to the sources used to train the chatbot.<\/p>\n \u201cI think what is good for our field is that we\u2019re not looking for specific facts, we\u2019re looking for design principles,\u201d he said as he explained the chatbot feature.<\/p>\n Once the billions of candidates have been screened and narrowed down to just a couple \u2014 or designed using the generative AI model \u2014 Aionics sends its customer samples for validation.<\/p>\n \u201cIf we don\u2019t get on the first round, we iterate and we can run some clinical trials to prove it until we get to the winner,\u201d Sendek said. \u201cAnd once we find the winner, we work with our manufacturing partners to scale that manufacturing and bring it to market.\u201d<\/p>\n Curiously, this process is even being used in some novel areas like cement. Chement<\/a>, a startup co-founded by Viswanathan and that is also partnered with Aionics, is working on ways to to use renewable electricity and raw materials to drive chemical reactions to make zero-emissions products like cement.<\/p>\n<\/p><\/div>\nElectrolytes, meet AI<\/h2>\n
Enter generative AI<\/h2>\n
Picking a winner<\/h2>\n