Chapter 1: What is the main topic discussed in this episode?
An AI agent can process several tens of thousands of papers a day and then has perfect memory of all the content. So that can be reduced from a month to on the order of minutes. Instead of human scientists, you have what's called ALAB, autonomous lab. It's basically a high throughput robot that will do 5,000 formulations in one morning.
When you give that to an AI model, it will give you about a thousand parameters. We can't really interpret them. It's like a different language, not meant for us human species to understand, but it works.
Welcome, humans, to the Neuron AI Podcast. I'm your host, Corey Knowles, and I'm joined, as always, by the undefeated champion of One More Thing, Grant Harvey. How are you today, Grant?
I'm good. I'm good. It's a little rainy out here in Southern California, which is uncommon. So if you hear the pitter-patter of rain, that's what's going on.
Then today I win the rent lottery, I'd like to say. It's beautiful here.
Wow, that's rare.
I know, right? Right? Well, we'll be joined here in a moment by Dr. Chi-Chao Hu, founder, chairman, and CEO of SESAI, a company working on lithium metal batteries and a more transparent EV battery supply chain. With joint development agreements in place already with General Motors, Honda, Hyundai.
And maybe more. We'll find out. Now, if you're wondering why this matters for AI, SES AI actually uses AI agents to discover new battery materials. Their platform, Molecular Universe, compresses years of material research into minutes. And they also use AI on the manufacturing side to catch defects and predict battery health.
It's a great example of AI solving a hard physical world problem, not just a digital one. But first, please take a second to like and subscribe to the channel so we can keep bringing you the most interesting people in tech and AI. And with that, Dr. Hu, welcome to The Neuron. Thank you both for having me.
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Chapter 2: How does AI accelerate battery material research?
It's basically a high throughput robot that will do 5,000 formulations in one morning. Yeah.
Instead of- Lifetimes of work for one human.
Yeah. Yeah. And then it's like perfect accuracy, no error. So that can reduce the filtering from, again, several weeks, another several weeks, even months to just days. And then the last one, probably the biggest bang for the buck is the validation. Validation takes a long time. It takes several years traditionally.
So once you have enough data and you can train these machine learning models, you only need to capture just the first probably two weeks of testing. And then you will know, you will know it's end of life. So, so each basically take each phase and then you can shrink what was originally years now to weeks, weeks, if not days.
So this does away with the whole idea of having to actually test a battery for eight physical years. It's able to do this in like a controlled, analyzed setting.
Yes, yes, yeah. That's amazing. That's amazing. We've seen this technique being developed and deployed in life science a lot. So in R&D, when you develop a new material, you have to go through lots of trials and errors. Say you go through 100 times that don't work, and then that 101st time works. So that first 100 times, you can really use this process to accelerate.
And then that final trial, the last trial that actually gets you the breakthrough, that one, of course, you can still take the time, do the full testing, do all that. But just the process before that can be much faster.
I guess that explains why, like, Batteries essentially went the better part of a century with pretty minimal advancements. Isn't that right?
Yeah, almost no. So just put things in context. The materials that are used in batteries, a lot of times these are small molecules, small organic molecules. Then in the universe, there's about 10 to the 60th, six zero possible small molecules. And then since the 1990s, the last almost 40 years, the battery industry only screened about 10 to the 3rd. different small molecules.
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Chapter 3: What challenges exist in traditional battery development?
market is a moated market in the sense a lot of cars from Asia, it's hard for those to come in without a tariff. So that also changes the market.
So that's not even a battery science constraint. That's just a business, political, economic, everything else constraint, basically. Yeah.
Does the supply have an effect? I know that, you know, traditionally some of these metals are very geographically located and acquiring them is difficult, comes with a lot of struggles as well. Does that play a role in... pushing these things forward or the decision to stay with lithium, I should say.
I mean, it does to a certain extent, but now the supply chain is quite diverse. A lot of the lithium comes from South America, Australia. They get refined. in China and also some in Canada, and then they get assembled into batteries. So in terms of availability of this, it's not an issue anymore.
Of course, sometimes the raw material price fluctuates and that influence the cost of battery, but in terms of availability, no, it's not a limitation.
Okay. That's good. I was just curious because I know I was thinking of like with cobalt, there were struggles as people were looking in those directions and others. And I wasn't sure about specifically how the supply of lithium looked.
So thank you. Yeah. Cobalt is used in the foam. For example, in the foam, the cathode is called lithium cobalt oxide. It's basically all cobalt. But then in the EV, there are two types. There's nickel cobalt manganese, where cobalt is less than 10%. And then the other type is lithium iron phosphate. So it's actually cobalt-free. There's no cobalt in that type. So it's not a constraint anymore.
Okay.
That's good to know. And lithium was a constraint, but it seems like a lot of emphasis went towards making more net new mines for lithium and trying to make it very accessible over the last couple of years.
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Chapter 4: How does the Molecular Universe project work?
Yes, because if you only do the dry computation, it's not very accurate. I'll give you one example. If you just use models to compute, for example, melting point and boiling point of certain molecules, Typically, you're off by 30, 50 degrees Celsius.
But if you have actual data from the wet lab, like actual raw data, and then you use those to calibrate, then that error bar can shrink to maybe plus minus two or five degrees.
So does that create a feedback loop then where you're using the molecular mapping for the dry data, you're then getting wet data to validate, and then you can feed that wet data back to your map and create a more efficient map or a more accurate map?
Yeah. So the dry data really allows you to map a much bigger map. universe. You can compute, for example, 10 to the 8th, 10 to the 9th, pretty quickly. Wet data, you're talking about 10 to the 4th, 10 to the 5th, so significantly less than the dry data, but that's enough to calibrate the dry data. Okay.
So it's like, basically, would it be equivalent to tuning it? To tuning the dry data? Yeah. Exactly, yeah. And I guess, just so I understand, when we're talking about this map, is this a bunch of text data? Or is there 3D models involved when we're dealing with chemistry? Is it a mix of both? What does it actually look like conceptually? What the data look like? Yeah.
What does a map of the data actually look like? Are you dealing with 3D simulation models like that? Or is this all just a bunch of text of chemical compound combinations? What kind of consists of it, I guess is what I'm wondering. What does it consist of?
Okay, so the molecule database consists of just molecule structures. And the structures are in 3D, but then you can represent the 3D in what's called small strings. For example, water is H2O, and then you just write O. So you can represent a 3D structure with a string of letters, C, H, O, those letters. And then what we compute and what we measure are these properties.
The properties are just in these numbers. For example, melting point, boiling point, energy levels, viscosity, just numbers. So at the end, you end up with an Excel table of 10 to the 9th, 10 to the 11th, eventually 10 to the 60th smiles streams, and then all the numbers, all the properties. That's awesome.
Do you have a rough idea of how many of those... You're running in, you know, I don't know, a week, month, year. I feel like there are so many applications for this, like you mentioned, that it goes so far beyond EVs. This is important research for a decade.
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Chapter 5: What role does AI play in manufacturing battery materials?
So do you notice a significant difference as the models have improved? Since you've been doing this over the course of three years, you've obviously seen some pretty monumental leaps in technology over that period.
Yeah. Basically, the more data you give it, the smarter it gets. And I will say the biggest difference is once you've reached a sufficient amount of data that you teach it, then the model is able to give you results and forecasts that's almost spot on. So then you can really save a lot of effort. But you really have to teach a sufficient amount of data.
My concern with that approach, though, is that the current language models, they have a limit to their context, right? So you must be using something else, or you can tell me what you think about this. If you have this giant database of all this different molecular data, how do you make sure that it's considering absolutely everything when it's going to work here? Do you get what I'm saying?
It's considering everything in terms of. Like basically how do you prevent loss from happening with the context window when you're running an agent through this data? I guess what I'm wondering.
So when we have the raw data, we don't really teach that to a large language model. We use a foundation model because the large language models are really good when the data is in text format, but when it's in like Excel numbers, it's not as good. So we use that to teach. So there's two parallel approach on the database, the raw data from the lab, dry data and wet data.
We use that to teach a foundation model, no large language model. And then in parallel to build that intelligence, we take all the books, all the papers about this domain, and then we teach that large language model to learn how to search for it. So one is, so think of the database as the map, and that's not LLM. And then think of the LLM as the search engine. That is LEM.
So we don't feed that large database into the LEM. We feed that into the map, into the foundation model. And then to the LEM, we only feed a more limited list of properties.
And that makes sense because when you're doing a more specific run, you have a more constrained problem space. So you're like, okay, we know it needs to focus in this area because we're looking for this chemical property. That makes sense. Yeah, I guess that was the thing that was throwing me off is like, I know LLMs have a context limit of a million.
You're like, I know there's an answer and I just don't know it yet.
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Chapter 6: How does the guest envision the future of battery technology?
Yeah, yeah. Well, especially with, you mentioned data centers, I imagine they need to be very, very efficient with their power, right?
So this is very helpful. Yeah, yeah. A lot of times the data centers cannot predict what's coming down the pipeline. And data centers are actually quite different from a normal grid because you really have to... allow for surge in power. So if you have a huge job coming in, then you have to drain the entire battery in about two minutes.
And then that kind of super high power density battery, we have not seen. It's actually quite new. And it's got to be safe enough. And then also it has to have really high power density in the data centers. So on the supply side, it's actually quite challenging.
So obviously you're developing your own robots. I'm curious what your thoughts are in terms of whether you are potentially working on something like this or whether you just have general thoughts on the direction, how to actually give robots enough power so that they can be as efficient as possible.
It seems like it's a battery problem to me, but I'm curious if that's something you're actively working on or thinking about.
Well, so the robots that we're building are more stationary and then they are plugged in.
More industrial approach.
Yeah, exactly. It's basically like a machine with a robotic arm. We don't really build like a battery powered humanoid. We don't build that. But I think for batteries, I mean, we've... Actually, we have some humanoid customers where we supply the battery and then we're getting, so before it was about two to four hour runtime per battery, we're able to extend that to about eight hours.
Once you get to eight hours, then it's almost like a human worker, like eight hour shift, right? Like one shift, second shift. So then, okay, then give the robot a break, right?
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Chapter 7: What are the implications of AI in energy storage and EVs?
Anything exciting that you want to touch on before we go?
I mean, a lot of the batteries and battery backup for data center we're working on, it's actually quite interesting. So we have one universe that's powered by a data center. And then one universe basically maps the universe and it comes up with these molecules. And then we use that to put them back into batteries. And we use the batteries to power these data centers. So it's almost like a loop.
Yeah, that's awesome. That's cool. Well, I'm sure you're going to be busy for years with all of the data center projects you're working on.
A lot of work to do there. Dr. Hu, what's the best way for someone to keep up with what you all are doing and go learn more?
Yeah. So we have, they can follow Molecular Universe. It's molecular-universe.com. Yeah. And then also once in a while, we send out these.
Awesome. Awesome.
Awesome. Very cool.
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