Materials Science
topic on 4 shows · 11 statements across 7 episodes
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11 statements about Materials Science, every show
Beam: Materials science is harder for AI than biology
“Well, I think materials are harder. So they have the benefit of like great simulators like that we don't have in bio. Like, in material science, you don't have the, like, mature, high-throughput automation that you have in biology. For me, materials as a subje…”
Krause: Physical synthesis and testing are the only ground truth in materials
“In materials, the ground truth is the material itself. You have to be able to make it, you have to be able to test it and characterize it, and then you have to really, at one point, be able to see if it can go into a real application if you're going to have it…”
Kulik: AI for materials is at 'ground zero' on manufacturing processing
“Most people who actually work on
Getting materials to the device scale, say something that would be in your television or something like that, is they will tell you that it's not just the material, it's the process.
And I think we're at ground zero.
We're nowh…”
Gil: Foundation Models Will Achieve Physics and Math Breakthroughs
“I think you know, a third area is the next set of foundation models are going to come, and by that I don't mean the Neolabs and the Next Gen LLM, which of course will happen, but I mean, physics, materials, science progress by models, math progress. And I thin…”
Mitchell: Materials innovators must build entire systems to convince customers
“When you have a materials level advancement, as the person who has that material level advancement, you really have to make the system to convince people that you have the solution.”
Çubuk: Deep expertise optimizing existing materials hinders discovering radically different ones
“The better you know a system,
The more you can continue optimizing the system, but it doesn't necessarily mean that knowledge will help you discover something different.
And I think this is probably why a lot of important discoveries are serendipitous, because…”
Çubuk: AI simulations will never completely eliminate physical lab work
“So I think, I can't imagine a future where we completely eliminate lab work. Because, first of all, we don't know if quantum mechanical simulations will ever become good enough to correctly predict experiments, you know, all the time.”
Çubuk: Physical materials experiments are as noisy as computational simulation errors
“In material science, I think one of the issues is the experimental data is actually quite noisy. So, you know, this is something that you might hear often that simulations and DFT isn't very accurate, and that's true, but maybe one thing that People don't noti…”
Çubuk: Materials science lacks a CASP-like benchmark database for machine learning
“And I think now, maybe because now machine learning really needs this high precision, large data set, there are these bigger efforts trying to create a CAASPP-like database, but it's not there yet.”
Çubuk: Deep learning scaling laws apply to quantum mechanics and materials
“The more training data you put into LLMs, the better results you get, and how much better your results are actually predictable. It's kind of like a power law. This comes back from, you know, a paper from Baidu Research from back in 2016, I think, and it seems…”
Preskill: Quantum computers will impact chemistry, carbon capture, and materials
“The Ones which will affect everyday life, I think, are better methods for understanding and inventing new materials, new chemical compounds. Things like that can be really important. You know, if you find a better way of capturing carbon by designing a better …”