You know that feeling when you're sitting in a chemistry class, watching the clock, praying for it to end?
Yeah, I was that student.
Fast forward to today: I just spent an hour with Prof. Hemali Rathnayake (Professor @ University of North Carolina at Greensboro and Co-Founder @ Minerva Lithium), and she made materials science so captivating that I actually forgot to check my watch.
Wait, Aashka... isn't your podcast about AI? Why materials science?
Fair question.
Well, lemme ask you this: What's the ONE thing that could stop AI by 2027?
It's not compute. It's not regulations. It's not what you think. The answer lies in material science.
Lemme give you a hint: China controls ~70% of it. The US produces less than 4%.
The plot twist? The country with the resource doesn't have the tech. The countries with the tech don't have enough resources.
This is the geopolitical chess game nobody's talking about.
Watch on YouTube; listen on Apple Podcasts or Spotify.
About Hemali Rathnayake:
Dr. Hemali Rathnayake is a materials scientist and nanoscience professor at the University of North Carolina at Greensboro who co-founded Minerva Lithium, revolutionizing sustainable lithium extraction for AI’s future. Her breakthrough adsorption-based technology produces battery-grade lithium carbonate from hard-rock deposits in just 48 hours without water waste—advancing from prototype to pilot scale for commercialization by 2027. Her expertise spans AI materials discovery, rare earth elements for quantum computing, semiconductor development, and next-generation cooling technologies for AI data centers, with research funded by NASA and the Department of Defense.
Episode Summary:
Lithium extraction, AI materials discovery, battery supply chain crisis, and quantum computing materials explored. Aashka Patel and Hemali Rathnayake discuss why lithium is the real bottleneck for AI data centers, not chips. Learn about Minerva Lithium’s 48-hour water-free extraction method, the coming lithium shortage threatening AI’s future, and why recycling e-waste is critical. Discover how AI is revolutionizing materials science by finding thousands of new compounds, room-temperature superconductors, and next-generation cooling technologies for AI chips. Topics include rare earth elements powering quantum computing, sustainable battery innovation scaling by 2027, AI-designed semiconductors, and the 5GW data center lithium requirements. Expert advice for future material scientists adapting to AI advancements in energy breakthroughs and the Manhattan Project-level challenges ahead.
Timestamps:
00:00 AIR Bites/Precap
02:26 The Real Bottleneck of AI Isn’t Chips — It’s Lithium
04:56 Inside the Hidden Bottleneck of America’s Battery Supply Chain
06:01 Can Innovation Fix the Lithium Crisis? Meet Minerva Lithium
06:55 How Lithium Is Actually Extracted — and Why It’s So Dirty
08:52 A 48-Hour Lithium Revolution: Extracting Without Water Waste
10:03 From Lab to Market: Can Sustainable Lithium Scale by 2027?
12:03 How Much Lithium Would a 5GW AI Data Center Need?
13:57 The Coming Lithium Shortage: Can Recycling Save AI’s Future?
16:42 Why AI Data Centers Demand a Whole New Kind of Battery
18:50 AI in Materials Discovery — A Revolution Already Happening
20:50 Can AI Help Discover Room-Temperature Superconductors?
22:05 How AI Found Thousands of New Materials We Missed
25:39 Quantum Computing: The Next Material Crisis
26:48 The New Oil: Rare Earths That Power Quantum & AI
30:28 The Secret Materials That Could Cool AI Chips Safely
33:34 Will AI Design the Materials to Build Itself?
35:05 By 2027: What’s the Next Big AI Energy Breakthrough?
36:40 If You Had a Manhattan Project Budget for AI Energy…
38:07 Advice for Future Material Scientists
42:52 Outro
Transcript:
Aashka Patel (00:04)
hello and welcome Hemali to On AIR with Aashka let’s dive right into the questions. every AI lab is racing towards building AGI and the real bottleneck isn’t the chips or even the data centers, it’s lithium because to
power AI at scale, we need massive battery storage and that comes from lithium China refines 70 % of the world’s lithium. So they control the entire supply chain from mining to battery grade material energy without storage is useless and as per Mark Zuckerberg, energy might be a bottleneck to the current AI advancement.
Here’s the question, if China controls the lithium that powers batteries that stores energy that runs AI, so why doesn’t China win this race by default?
Hemali Rathnayake (00:53)
So it is because China can control the supply chain, that production of lithium. But is China is controlling the battery technology and energy storage technology. So most of the successful energy storage technologies and battery technologies lies in within US and the Europe. So of course that these countries Europe and US mainly
that’s we’re facing in the supply chain and in the lithium, but the battery technologies relies in the within across the other continents. So because of that, China cannot be the lead in the AI storage in the market.
Aashka Patel (01:38)
Okay, so like can you elaborate more on the battery technology, what do you mean by battery technology?
Hemali Rathnayake (01:44)
Yes, so
let’s take for example Tesla EV battery technology. So, that technology is developed in US. Of course, China has a couple of battery technologies, for example, waveform lithium, that’s a sodium ion battery technology, that’s where they are leading for most of the EV, for example BYD technology.
And but the lithium battery technology is leading countries, US. So because of that, so China, even though China has the lithium, so they don’t have a technology to develop battery production over there alone.
Aashka Patel (02:31)
Okay, okay, okay. Got it, got it. But like, does US also have the mining capabilities and the extraction and everything along with the technology or is it dependent on others?
Hemali Rathnayake (02:43)
That’s
where the bottleneck in US and the surprising bottleneck US is facing because domestic production of these raw materials are minimum. So I would say as US produce lithium less than 4 percent 4 percent versus China is the lead.
Aashka Patel (02:47)
Okay.
Okay.
Less than 4%.
Hemali Rathnayake (03:09)
in lithium production over 70%. So that’s the bottleneck. So I think that’s why it’s one country maybe cannot lead in their AI ways and for the storage. So I think most of the both countries or the whole continents and supposed to come together and then work together to solve the supply chain challenges.
Aashka Patel (03:17)
Uh-huh. Mm-hmm.
Okay, okay, okay. Got it, got it. So, you mentioned the bottleneck in the US. So, you have a company in the US called Minerva Lithium. it’s on a mission to transform how we source one of the world’s most critical clean energy materials that is lithium. So, can you explain briefly what are the different ways of lithium extraction and how is your way more sustainable than others?
Hemali Rathnayake (04:02)
Yeah, so the lithium comes with different feedstock. The main feedstock lithium is coming from hard rock, that’s the mining from hard rock. And US hold some of the hard rock mining, for example, North Dakota, and especially North Carolina. So that’s the major resource lithium mine in US currently.
But other than that, lithium can also extract from brine. So for example, salt rake brine, and also there’s some of the packing produced brine. So those are the two methods that we can refine, get the lithium out. However, in the current lithium mining for the hard rock or the brine, it takes time. For example,
put into perspective. So the heart of mining need a lot of energy. So it is very energy consuming and then environmental impact is high. People hate mining, right? and then for brine, so it’s a major way producing lithium from brine is solar evaporation. That’s take 18 months to two years to get the lithium out.
Aashka Patel (05:26)
MG! That’s
a longer time, yeah?
Hemali Rathnayake (05:29)
long time. So,
it also includes chemical precipitation. So, but recently, so people have been developing absorption, selective absorption processes. So, to get the lithium directly extract from brine, that’s called direct lithium extraction. But it’s still all these processes has lot of disadvantages and advantages. But the main disadvantage is
any of these processes, it needs energy and the carbon emission is high. And most importantly is the fresh water usage. So for example, put into a prospective, I want to produce one metric ton lithium carbonate equivalent. For all these processes, you need about 30,000 gallon of fresh water.
Aashka Patel (06:26)
OMG!
Hemali Rathnayake (06:29)
So that’s where Minerva Lithium plays a role. So the
Minerva Lithium Nanomosaic technology we developed, it’s absorption process, but it works in room temperature. So it is environmentally friendly. You can extract lithium within 48 hours. But this is not exactly the direct lithium extraction. We call it as a passive lithium extraction.
What that means is we take the impurities and concentrate the lithium, then there is no use of fresh water, like 30,000 gallons per metric ton. So we’re down that to 5,000 gallons per metric ton. That’s a huge...
Aashka Patel (07:15)
Oh, that’s a
considerable amount of reduction.
Hemali Rathnayake (07:20)
reduction. and then we don’t have a carbon emission. We don’t use the higher energy processes. So that gives us more sustainable, environmentally friendly method to extract lithium from brine resources.
Aashka Patel (07:37)
That’s interesting, like literally. So, how is it going right now? what’s your business model,
Hemali Rathnayake (07:44)
Yes, so and currently and we actually we just wrap up our technology showcase last week. That was October 2nd. So we had our technology showcase and we asked all the leading investors to come in and take a look at our technology. we were able to, we are at this stage in the pilot scale. We were able to validate.
Aashka Patel (08:03)
Mmm. Mmm.
Hemali Rathnayake (08:10)
at the pilot scale and with the minimum viable product, our continuous filter process. Now currently we are in the seed fundraising stage for up to 10 million to get into the commercialization by 2027.
Aashka Patel (08:16)
Okay.
Thank you.
Nice, nice, nice. So, the process sounds very exciting and environment friendly. So, the lithium that is extracted out of this process, is it like direct battery grade lithium or does it need more processing or something
Hemali Rathnayake (08:45)
Yeah, so the the Minerva Lithium Technology is from extraction to refine into battery grade. It’s a whole supply chain. So we what we do is we extract and then we directly refine to battery grade lithium carbonate currently. So our battery grade lithium carbonate purify is 99.95. That’s the
maximum that’s the normal purity that needed for normal battery but if you want to get into the EV battery technologies so you have to reach to the 99.99 so that’s purely for example Tesla EV batteries using for so but in general it’s 99.95 is the market available lithium carbonate purity.
Aashka Patel (09:38)
yeah, but I’m pretty sure you will reach that purity level of EV batteries too. Yeah. So from a materials perspective, like if we wanted to flip the five gigawatt data center to solar.
plus storage tomorrow, what’s the actual lithium requirement and is that even physically possible with the current refining capacity that you have or combined technologies have
Hemali Rathnayake (10:04)
the within US or in overseas and with the current refining technologies, they are traditional technologies, right? So because of that, they need more energy to extract and refine. So because of that, so that’s where the bottleneck and always that’s the challenge. So with the current refining technologies and
If we combine these traditional pathways with the innovative technologies like the Minerva lithium technology, so we may able to meet the capacity that needed. But however, if we talk about 5GW, so let’s put into the perspective, right? So if we put into the perspective, so therefore single rack mounted battery for 5GW you need about 4,800 volt
hour capacity, battery capacity. So what that means is it’s basically you have to produce at least 400,000 metric ton of lithium per year to meet at least one rack of battery capacity for 5GW.
Aashka Patel (11:23)
my God. So it seems almost impossible to meet the demand. what’s your take from the numbers?
Hemali Rathnayake (11:32)
So it is impossible to meet the demand from the existing natural resources. That’s why as a scientific community and the industry partners, they should look into recycling. So there are a lot of battery e waste, but there is no technology at the commercial scale you can extract these.
Aashka Patel (11:48)
Hmm.
Hemali Rathnayake (12:00)
critical material from used batteries, recycled batteries. So if we find a path to recycle and extract this not only lithium, other material, cobalt, material exactly going into the cathode, and I think we should look into more of the recycle and recovery these critical minerals from recycled batteries.
Aashka Patel (12:12)
Yeah, other important materials are also there, right? Yeah. Yeah.
So like from your experience or from whatever you have heard how many efforts are put or research is put into this recyclability because I do have some stats on that the AI companies are upgrading their GPU clusters almost every 24 months as new Nvidia chips are coming out. So there’s a tsunami of e-waste that is getting created.
Hemali Rathnayake (12:47)
Mm-hmm.
Aashka Patel (12:52)
So, of course there is a big opportunity to solve this problem. what are the advancements happening there to solve this problem.
Hemali Rathnayake (13:01)
Yeah, so the complexity is because when you have the recycled battery, you get this powder that called black mass. So the black mass contain all the anode, cathode, all the mixture of materials. So now you have to extract these minerals purity and take it out from black mass. So there are a couple of industry leading in this black mass technologies.
and then find to capture these, get these, extract these elements out of black mass. So it’s still in the lab scale or the pilot scale in the research and R &D developments. So, and but there are a couple of technologies that people have been successful. That’s one is the electrochemical method, separation method.
Aashka Patel (13:42)
okay, not at an industrial level.
Hemali Rathnayake (13:54)
I
think that’s one of the technologies currently leading that you can take this black mass and extract these critical materials. At Minerva Lithium also we’re looking into whether we can use our technology combined with electrochemical process to larger production of lithium from this black mass.
Aashka Patel (14:16)
Black
mass, yeah, yeah, that would be revolutionary like literally. Yeah, yeah, yes. So data centers these days are running 24, 7, 365 days at 90 % plus load factors. But the batteries serve a different role than typical grid storage. So what makes these data center
Hemali Rathnayake (14:20)
Mm-hmm. Yep.
Aashka Patel (14:38)
battery requirements uniquely demanding from power density to response time to reliability compared to standard utility scale or like as you mentioned the EV batteries. So what is the difference there?
Hemali Rathnayake (14:54)
So it’s the grid capacity, right? So you need very large grid capacity for these data centers or the 5GW data centers for the AI. So I think that’s where the huge problem. So it’s not only that these battery technologies needs to be moved forward and advanced, but the grid capacity.
Aashka Patel (15:10)
Hmm.
Hemali Rathnayake (15:22)
In terms of the grid capacity, we need to advance in the grid capacity to reach it to where we wanted to be with the data centers and use it. So that’s the huge bottleneck in grid capacity.
Aashka Patel (15:37)
So, that, so as per what you are saying like that is not a problem to be solved by the battery technology, but it is a problem to be solved by the grid capacities and everything that is considered, right.
Hemali Rathnayake (15:50)
Uh-huh. Yeah, grid
capacity and the infra structure needs to be go side by side. And that’s why so there are complexity, not only the battery supply chain. And then it’s a manufacturing capability, infra structure, grid resilience. And all those needs to, you need to work alone with all those side by side to
which way we wanted to be with this trend in AI or the 5GW capacity.
Aashka Patel (16:24)
Yeah. Yes, yes, yes, yes. So yeah, Google DeepMind claims that AI is going to revolutionize the materials discovery like on Google DeepMind’s podcast itself, I heard Pushmeet Kohli talk about two millions of
Hemali Rathnayake (16:24)
It’s eventually an emergency, so...
Aashka Patel (16:43)
materials that AI has predicted could be stable before it was something 20,000 materials that we as humans were able to find out to be stable but 2 millions are what AI models have predicted but of course it requires
human testing like are they actually stable and sort of things. So in your research on nanostructured materials, battery components, MOFs are you seeing genuinely non-obvious breakthroughs or is AI mostly optimizing the existing chemistries faster? Like is AI actually helpful in new materials discovery?
Hemali Rathnayake (17:23)
So the AI, definitely, definitely AI is helping the material discoveries and also the search in the existing database, right? So you cannot do in the brute force method.
Aashka Patel (17:38)
yes.
Hemali Rathnayake (17:39)
So,
the AI can help you to search the database in the existing material. Sometimes existing nanomaterials we never explore for battery aspect or the energy storage. Maybe they use for them for different other applications. So, think that having AI apply into the material platform is
Aashka Patel (17:56)
Yeah.
Hemali Rathnayake (18:04)
been has been beneficial and it advance finding new material or develop some of the existing material and optimize their properties to get into the way we wanted to be. I definitely agree so the AI has been helping for material discovery.
Aashka Patel (18:24)
Got it, So, like I read this somewhere, the holy grail of material science is to discover a room temperature superconductor. Like, correct me if I’m wrong. so looking at the current scenario and as you mentioned that AI is definitely helping in the material science discovery. So, do you think AI can help us get to that holy grail of material sciences?
Hemali Rathnayake (18:49)
Yes, definitely. An AI can generate the hypothetical or the new material, right? And then eventually, of course, someone has to make the material. I think that’s where there is a timeline. And so it takes longer time to develop the material and then synthesize the material and getting into the commercial scale.
But definitely the way that we have been adopting AI programs and algorithms to find the material discoveries, and I think it is possible. this my wish is maybe by 2030 and we may have something that room temperature material discovered for superconductivity.
Aashka Patel (19:40)
Okay, nice, nice, nice. So far like in your experience, because you are also a professor, right? So in your experience and in your discoveries, if you can put a number to like, okay, AI predicted these many stable crystal structures or new alloys and out of them, like how many of you like human tested and then found out that they are actually stable materials that
wasn’t maybe humanly possible to discover them are those kind of
aha moments that you had like for the lack of a better word
Hemali Rathnayake (20:18)
Yeah, so I think it’s a couple of AI models that help to develop is the some of the high energetic alloy material. There are a lot of high energetic alloy materials has been advancing in the scientific community. If you take a look at publication records in the high energetic material, so they
been alloys different like partial stoichiometry, right? If they are not like formulation is easy, but those has been advancing in this regime. So it basically, and then if you take a look at metal–organic frameworks, of course, and this year we got the Nobel Prize for the metal–organic framework. So we do have 84,000
synthetic metal organics framework that’s known in the database. But we barely explore them to use in the magnetism, so paramagnetism, superconductivity. I think the AI models that develop in this metal organics framework platform has been tremendous. And then you can actually search the existing database.
So those are some of the materials that AI helped to develop, like high energetic entropy material. and searching the database in the metal organics frameworks to apply them into different applications in, for example, solid state electrolytes in the lithium ion battery. For example, anode and the cathode materials. So those research has been done with the
side by side with AI.
Aashka Patel (22:10)
Okay, okay, okay, got it. So the database that you are describing, is it like, how is it kept updated? Like is it a central repository used by the universities of US or like how?
Hemali Rathnayake (22:22)
Yes,
there is a central depository and then owned by UC Berkeley and that’s where Dr. Yagi has been for the one of the Nobel laureates for the metal–organic.
So, they have been updated in this repository And also the Cambridge Crystallography Database has all the crystal structures of these materials that one can utilize to develop algorithms or the AI models to search the properties or create the properties with the optimized structures.
Aashka Patel (22:58)
Got it, got it. So, like the Cambridge one and the UC Berkeley one that you mentioned, like are they open source? Okay, okay. Then that’s great. Like yeah.
Hemali Rathnayake (23:05)
open source.
They are open source.
can access. are open source.
Aashka Patel (23:14)
Got it, got it, yeah, yeah, that’s a great initiative taken by both the universities. let’s jump on to quantum computing for a while. So, quantum computers need totally different materials, superconductors, exotic stuff, right.
quantum takes over, are we just swapping the lithium bottleneck for a whole new set of material problems that we haven’t solved yet?
Hemali Rathnayake (23:38)
Yes, so for the quantum computing you need high magnetic material, need the rare earth elements and that goes to quantum computers. So that means we are opening another supply chain bottleneck within
Aashka Patel (23:40)
What are those materials like?
No.
Hemali Rathnayake (23:59)
and across globally. yeah, so people talk about quantum computing, but you need the rare earth elements to get into the quantum computing, especially for magnets. So that’s where the bottleneck and how you can create, get these rare earth elements for magnets for the quantum computing. Yep.
Aashka Patel (24:01)
Okay.
Yeah, yeah,
makes sense. So currently like if you were to look at the world level like where are these rare earth elements available right now in the world?
Hemali Rathnayake (24:34)
So again, when it comes to rare earth elements, it’s the leading countries are, one is China, other one is Russia, and then also African continent. So those are the ones that has more of the rare earth element deposits. also recently, if you take a look at like critical mineral leaders, so there are 10 countries and including India. So India has the deposit of rare earth elements.
and then all other African continent, Tanzania, for example, those countries has rare earth elements. US also has about 1.8 million tons of the rare earth elements in US. then, you know, it is mining and extraction and putting them into the commercial scale and the production is the best way.
just that we don’t have a domestic infra structure developed.
Aashka Patel (25:33)
Yeah, so like for these rare earth elements like is research happening on to how to extract and how to better utilize them for quantum computing like all over the world?
Hemali Rathnayake (25:40)
Mm-hmm.
Yeah, there are
separate research domains looking at the rare earth element extraction and refining and then creating the magnets that goes directly into semiconductors and including quantum computing. And there are leading companies that recently start in this domain.
So, again, so they are either in the lab scale or the bench scale or the pilot scale. And the commercial production is really, really low in compared to some of the leading countries like China and or maybe Russia So, those countries, I think they are the main countries that and Australia.
leading these rare earth elements exporting.
Aashka Patel (26:39)
Okay, okay. So like the reason why I asked this question is to see if there is a scope of Minerva lithium in rare earth elements or not. So like viewers watching this, if they are interested then they can go and build a Minerva lithium for rare earth elements. So yeah, yeah.
Hemali Rathnayake (27:00)
Yeah, yeah, so we are
looking into it and because since this technology can separate, if we are successful separating cobalt, nickel and lithium from black mass or the recycled material, I think that’s the path to move forward and we validate that technology and the process.
Perhaps, Minerva lithium could lead into rare earth elements because they come with very low concentration and then they come with the hard rock stage. So you have to refine them, separate them from the other impurities and other minerals. So that’s maybe the future.
Aashka Patel (27:52)
yes, yes, yes, yes. Yeah, this is a revolutionary breakthrough. Whoever gets the minerals from this black mass. So yeah,
Kudos to anyone who solves it because that’s the demand of this age. yeah, let’s move on to the next question. Data centers are moving to liquid cooling for AI chips. So most liquids are either flammable, corrosive or terrible heat conductors. So are there noble fluids or phase change materials from nano science perspective that could revolutionize how we cool AI hardware?
Hemali Rathnayake (28:31)
So the yes and there are research that people have been looking into nano scale material development for instead of liquid cooling is they are either aerogels or the they are solid state and or they are in the liquid and the liquid and the solid in the middle phase so
The most of this liquid, coolant material are either as you mentioned, they are ionic liquids or they are high-flammable liquids. But now people look into more of the, in the liquid phase that they can be solidified quickly at the room temperature. They are called deep eutectic solvents. And this is one of the people trying to.
In the nano scale, we are trying to look at whether we can use these deep eutectic solvents and mix with composites with the nano materials, for example, zinc nanoparticles. So because they have a very good thermal properties and then heat absorption properties. So those kind of materials people have been looking into.
And yeah, so one of the novel is these deep eutectic solvents domain. then people are trying to develop these deep eutectic solvents from sustainable readily available materials like urea, choline chloride, and ascorbic acid, citric acid. Those are mixture of two organic material.
and then when you mix it, they became liquid but
they are very high temperature liquids. So that address the flammability of ionic liquids.
Aashka Patel (30:29)
Okay. Yeah, that’s very interesting because like before I started researching for this podcast, I only had the idea from the news that water is, fresh water is getting used for cooling down these like computing systems in the data centers and as a coolant and basically I had no idea about these different materials. So yeah, it’s very interesting to know because
Hemali Rathnayake (30:44)
Mm-hmm.
Aashka Patel (30:55)
I thought why are we using these limited freshwater storage that we have as the planet earth? So this is very interesting to know like, okay, other coolants are also getting used.
So, Meta and Google are now using AI to design their own chips. So could AI eventually design the materials to build itself like better semiconductors, better interconnects, better thermal materials? Like you mentioned that from material science perspective, it’s helping a lot, but like is this possible? What’s your take on that?
Hemali Rathnayake (31:33)
Yes, it is possible. mean, we exercised using AI eventually to develop the COVID vaccine, right? And within less than two years, with less than a year, we were able to develop the COVID vaccine because of the AI. So, and as I mentioned, rather than the Brute Force method, and so...
AI can help and better develop if we develop better algorithms for AI models, language models, and it is possible we can use AI to develop the material revolution. There are a lot of research now driven towards this, right? We’re a lot of data centers, AI-driven data centers, AI-driven...
material hubs in US and the national labs are on this game, right? So I think in the future, is rather than we designing the material from the scratch, I think the AI will accelerate the material revolution. I believe it.
Aashka Patel (32:40)
Yeah, yeah, makes sense, makes sense. So if you could make one bet, not a wish but a genuine prediction if you were to make. So what will be the biggest surprise about AI energy infrastructure when we have the same conversation in 2027?
Hemali Rathnayake (32:58)
2027 is not far away, it’s two years. Exactly, when we come to AI energy in fra structure and within 2027, we’re going to have again the conversation about
Aashka Patel (32:59)
Far away, yeah definitely. That is why I asked because longer timelines they are harder to predict, right?
Hemali Rathnayake (33:16)
grid capacity and the grid resilience. So I think people need to pay attention and policymakers and legislatures and funding agencies and federal funding and all those they need to think about how we can build grid capacity and the grid infrastructure resilience. So that’s going to be the discussion again we’re going to have and
Maybe the battery technologies can be advanced and energy storage can be advanced, but building them into the grid resilience is going to be a problem.
Aashka Patel (33:53)
Ultimate problem to be solved. Got
it, got it. Hopefully the Stargate project solves it to a greater extent, right? Because a lot of money is getting poured into that project and a lot of land area is also allocated to that. So, hopefully we see a better solution there.
Hemali Rathnayake (34:03)
huh.
Aashka Patel (34:15)
Like as we talked about the Stargate project that is of the scale of Manhattan project, right? That happened during the World War II. So if you could assemble a Manhattan project style team with unlimited funding, you have as many dollars as you want to solve one materials challenge that would unlock the next decade of AI, what would you work on?
Would it be solving the battery problem or room temperature, superconductors or something entirely different? Like what’s your like one material
Hemali Rathnayake (34:52)
So the energy demand is the main cause for all the global chaos, right? And everybody is chasing on energy. So I would say if we had unlimited funding, funding, it is solving the energy problem.
Aashka Patel (35:10)
Funding available. Yeah.
Hemali Rathnayake (35:18)
to
better batteries and then also the room temperature superconductors.
It’s kind of that would be that more of the leading projects we should think about when the energy demand is able to resolve. So I think the world will be in a better place.
Aashka Patel (35:42)
Yeah, yeah, makes sense, makes sense. So, as you mentioned about like AI having immense potential in material sciences and material engineering. So, like if someone is studying material sciences or material engineering today, like what would be the one piece of advice that you would give to them for them to survive in a world where AI would
mostly be helping us do discoveries and everything.
Hemali Rathnayake (36:11)
Yep. So if you are a student or going to be in the material science world, so you have to be continuously update yourself and adopt AI to utilize better in your research and the material discovery. so AI can be a great tool.
for your research, for your material discoveries. think it’s Adaptability is very important. And then where are we going that’s without AI and so you cannot anymore survive We are moving to AI world, right? So that means we need to be adapt to AI and understand
can better and as a tool, not just the figure writing on it. So you can as a better tool to utilize for your research well. So I think that’s my advice. you can use machine thinking to
better understand and guide your research tools to have better innovation. So that might take home message. So innovation can accelerate if you use the AI tools in better way.
Aashka Patel (37:32)
accelerate yeah definitely
Got it, got it. So is there a specific toolkit that you have as a researcher or like that you would recommend to a researcher in terms of AI tools?
Hemali Rathnayake (37:46)
No, I usually
collaborate with the AI models but there are lot of packages and people have been using them. I always try to understand rather than using applying the packages and I usually go with more of the concept-based and developing the algorithms, right?
I always ask students to don’t use the packages and see whether you can contribute developing the algorithms and add these packages. in my research, we always develop the algorithms and then we use those algorithms to run the
models using these packages. rather than you adopting the packages into your model, your need, think it’s from the scratch you develop the algorithms and then apply those into the AI models is the better way to understand advanced research.
Aashka Patel (38:49)
Yeah, got it. So, just to clarify for the viewers by packages you mean Python packages or Python libraries right of that. R and okay got it, mix. Okay.
Hemali Rathnayake (38:56)
Yeah, piping packages, other language model packages, yeah, so those are,
there are a lot of computational packages available and, but it’s constantly updating, right? And there are lot of packages that has been there, but my advice for researchers, when you use the packages, you need to ask, what’s this package? Why you add that?
Aashka Patel (39:22)
yes.
Hemali Rathnayake (39:23)
what’s your model
and what’s your boundary conditions and if this package is suitable for your research. People using these packages for right without understanding and what’s what’s the what’s algorithms behind these packages right.
Aashka Patel (39:41)
definitely.
for this material sciences, like I didn’t have much insight into this. I had to do a lot of research to ask you these questions confidently because I was never interested into these kind of material sciences and everything.
So thank you so much for enlightening me. Yeah, literally. It was like for me as the science lecture that I always used to avoid. But it was very informative and insightful at the same time. It wasn’t like the the boring professor lectures that I used to have. So thank you so much for making it interesting. Yeah, thank you. And let me stop the recording.
Hemali Rathnayake (40:00)
Yeah, those are really suspicious.
Welcome.
Okay.
