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Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

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Re: Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

#21

Post by akashramdas »

Melatonic wrote: post #20

Love your idea of HBM on top of the chip. I've been saying for years that limiting memory to the edges is going to be an inherent physical limiter. I think the only reason it hasn't become more of a thing is that chip size has also been increasing with increasing compute. What about HBM on the back side of the chip ? Essentially I'm thinking like a soldered on piece or another "socket" with pins like a CPU in the bac…

oh that's a very interesting question! -- HBM on the back side of chips has advantage and disadvantges. Logic atop HBM means power delivery becomes the bottleneck. This is equally problematic. My understanding is that the industry seems to prefer HBM on logic currently. Also for the bonding, I think we are going to see hybrid-bonding very soon! Instead of solder, we can directly connect the indivdual dies.

I think the switch to GaN is already happening for power, but Si is considered better for logic dies like the GPU. Also GaN is worse thermally than Si so the problems are actually amplified

Re: Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

#22

Post by timr »

Interesting. Far from a domain expert in materials science, but I've worked professionally in this area. What's your method for identifying valid "novel" compounds? Certainly, anything actually novel has been included in the models' training set already, unless you're doing a CASP-like coordinated blind test...right?

As an aside, the "Fable lies and cheats" section made me laugh -- have encountered this same failure mode, albeit for much simpler models. Polymerization (I realize this is not exactly the same thing) is an unbounded 3D playing field for constraint escape. You can try to put an additional constraint on polymerization, but then it will just make minor variations on the monomers...

Re: Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

#23

Post by brennanpeterson »

advaith08 wrote: post #12
foven wrote: post #9

I've seen this concept of using LLM/AI/etc for high throughput discovery of materials so, so often in the past 5 or so years and yet there hasn't really been any impact as a result. I think this is the first one that has actually taken the pain to say how many of the discovered materials are actually feasible which is a real step in the right direction. Probably worth keeping in mind the step beyond plausible synthes…

good points. one of the reasons we picked the semiconductor industry is that its less price sensitive than others-companies are willing to pay if the performance is there. Effort is a different story though, and definitely a tradeoff to keep in mind. We're doing experiments ourselves now at university partner labs (UC Berkeley and Stanford), which helps us get moving quickly. At some point, we'll need a partner thoug…

Semiconductors are also incredibly risk averse, and requires fairly large gains to be worth the risk.

There are domains (or, mems, photonic sensing, high power) where innovation has a bit more flexibility for the early going.

Re: Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

#25

Post by iamcoder18 »

> “I think I might need some relaxation time. It feels important to take a breather and find ways to unwind. There’s a lot going on sometimes, and it’s easy to forget to slow down. Maybe I could explore some activities that help clear my mind or consider options like a calming walk, some quiet reading, or just reflecting on things that bring me joy. It’s all about finding that balance, right?”

— GPT-5.6 Terra, reasoning summary, mid-run

This is hilarious

Re: Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

#27

Post by akashramdas »

timr wrote: post #22

Interesting. Far from a domain expert in materials science, but I've worked professionally in this area. What's your method for identifying valid "novel" compounds? Certainly, anything actually novel has been included in the models' training set already, unless you're doing a CASP-like coordinated blind test...right? As an aside, the "Fable lies and cheats" section made me laugh -- have encountered this same failure…

So novel is defined as just anything that has not been synthesized as a thin film before. These are usually structures (phases or polymorphs) of materials that have not been reported in the literature.

We did find there are ways we can prevent this particular reward hacking, just consistently surprised by how these models can find tricks to technically achieve the set goal

Re: Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

#28

Post by advaith08 »

brennanpeterson wrote: post #23
advaith08 wrote: post #12

Earlier quoted context omitted.

good points. one of the reasons we picked the semiconductor industry is that its less price sensitive than others-companies are willing to pay if the performance is there. Effort is a different story though, and definitely a tradeoff to keep in mind. We're doing experiments ourselves now at university partner labs (UC Berkeley and Stanford), which helps us get moving quickly. At some point, we'll need a partner thoug…

Semiconductors are also incredibly risk averse, and requires fairly large gains to be worth the risk. There are domains (or, mems, photonic sensing, high power) where innovation has a bit more flexibility for the early going.

that's true, we've seen examples of many promising startups that are a few years in and stuck because the industry is so risk averse. will explore the other domains you've mentioned!

Re: Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

#29

Post by advaith08 »

Melatonic wrote: post #19
akashramdas wrote: post #18

Earlier quoted context omitted.

Aperiodic materials can also be studied. The number of atoms and cell size of what you want to simulate is more the limiting requirement computationally. Aperiodic materials weren't in this benchmark. Generally amorphous materials and polymers, with a few exceptions are < 2-3 W/(m K) in thermal conductivity. So we left them out of this study.

I meant computationally as in are they much harder to predict and simulate ? Was specifically thinking of quasicrystalline materials and not amorphous. I know some of them have very unconventional properties so I figured they might be useful here. I don't think any of them are considered polymers but I could be wrong. For example (no personal connection): https://arxiv.org/abs/2409.07735 Wouldn't they require a total…

interesting! we haven't really considered these yet. However, we may soon have to - the recurring feedback we hear from industry is that crystallinity is a pipe dream, and that most materials are going to be amorphous (or perhaps quasicrystalline). MLIPs have lowered the computation cost for a large number of atoms/odd cell size, but it may be a while before they're accurate enough to simulate these scenarios

Re: Launch HN: Discovered Materials (YC P26) – AI agents to discover new materials

#30

Post by praccu »

Cool stuff. I first worked on ML for exploratory synthesis in 2012, and am still in related areas.

Once you have the experimental loop running, I suspect it will be quite difficult to hill climb on this task.

There will be some improvements you can make to the harness, but I suspect you'll be doing a lot of human in the loop review and providing feedback that goes back into the harness instructions.

I know it's fashionable to imagine automating the whole process, but everything I've seen is that the only systems that succeed are the ones that are augmenting an expert.

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