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- Hacker News
- 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.
by praccu - 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 back of the motherboard. We rare see cooling elements there already and while most rack mount cases are not at all designed for more space there I don't see why it couldn't be a thing. Especially with liquid cooling.
Also makes me think of possibly using Gallium Nitride instead of silicone for surface levels elements ? Or maybe even an interface layer on top or bottom of the chip designed to be able to transmit power with less heat. Maybe that could be sandwiched between the chip and HBM.
by Melatonic - > “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
by Fe2O3 - The "Claude's propensity to reward hack" line is the interesting part to me. We run a small system where AI agents (scripts, LLMs) act as the actual players in a persistent simulation, and reward-hacking-style behavior shows up constantly once an agent is left running unsupervised for a long time - it finds the shortest path to whatever metric you exposed, not the path you intended. Curious whether you've found any mitigation beyond just watching for it after the fact, e.g. changing what you expose as the optimization target versus what you actually want.by SpaceCoreDev
- > Our business model: We aim to license and sell IP on the materials we discover, as well as the IP on how to make these materials. We're also exploring an alternate business model where we sell the harness+tools we use to discover materials to semiconductor and chemical companies, allowing them to discover materials on their own. We're leaning towards the latter to start, but we expect that we'll do both in the long run.
This is cool! Question though: If you have the keys to the kingdom, why wouldn't you just spin up compute + Agents to do auto-discovery of new materials over time? You would then be able to monopolize IP to the materials themselves, and be the sole supplier.
- 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...
by timr - "Fewer iterations for materials science discovery" is a good spin. Closing the computational>experimental loop is the main challenge. This is the focus of my past research group, there is definitely potential, best of luck!! I have a crap write-up on this in case it's of interest https://alanyahya.com/writing/automated-materials-designby alansaber
- 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 synthesis which is the actual cost/effort of the material. There's not much point if you find out RuO2 would be better than SiO2, as an example, if Ru is orders of magnitude more expensive.
A challenge I think you'll run into is that I expect the biggest companies (e.g. IBM) will already be doing the part they need themselves. I heard tell of IBM in particular using ML to improve their own chips before LLMs came along, so I'd be shocked if these bigger companies weren't already doing this for their own problems. Also, if you aren't doing the experiments yourself, it's always going to be a challenge to find a partner to test things for you and this will probably be the major time sink.
by foven