[HN Gopher] Graph Networks for Materials Exploration
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Graph Networks for Materials Exploration
Author : reqo
Score : 137 points
Date : 2023-11-29 16:17 UTC (6 hours ago)
(HTM) web link (deepmind.google)
(TXT) w3m dump (deepmind.google)
| bee_rider wrote:
| > The GNoME project aims to drive down the cost of discovering
| new materials. External researchers have independently created
| 736 of GNoME's new materials in the lab, demonstrating that our
| model's predictions of stable crystals accurately reflect
| reality.
|
| It seems like a neat project.
|
| I wonder, though, what does an unsuccessful prediction look like?
| They successfully created 736 of the materials. I'm sure they
| didn't make 380000-736 bad predictions, hahaha!
|
| Would it be interesting to know about materials in their set
| where fabrication was attempted but didn't work out? Or maybe it
| is much more complicated than that; maybe it is assumed that
| there are crystals in the set that are basically impossible to
| fabricate for complicated engineering reasons, and but that's
| fine because it is just the beginning of the investigation.
| therajiv wrote:
| Are applications like batteries, semiconductors, solar panels,
| etc. bottlenecked by the number of available materials? Also, I
| wonder if the discovered materials are kind of "interpolating"
| between materials that are already known, or if they expand the
| convex hull in some way. (Though perhaps it's difficult to
| precisely define what the convex hull of materials is.)
| foota wrote:
| The linked paper from Lawrence Berkeley National Lab is almost
| way cooler, automated wetlab material science experiments:
| https://www.nature.com/articles/s41586-023-06734-w
| dekhn wrote:
| They look cool but are expensive, hard to set up and maintain,
| and rarely exceed what can be done for the same amount of money
| by a small amount of well-trained chemists.
|
| The arm in the figure 1-3 is probably $100K, before talking
| about the support contract and site integration.
| foota wrote:
| I think the novelty here is in the automation of it? If you
| (or let's be real, some eccentric billionaire) set up 100 of
| these and hooked them up to run 24/7, they could generate a
| stream of test results. If you can scale this up maybe you'd
| hit economies of scale?
|
| Shame there's no eccentric billionaires that love shiny
| projects with little hope of success. /s
| dekhn wrote:
| This is already happening all over the world across
| multiple industries. It's typically called lab-in-the-loop.
|
| I have been involved in projects with eccentric
| billionaires to build such things. It's challenging to make
| forward progress in a meaningful way (IE, beyond a press-
| and-paper prototype), and often the reasons are entirely
| banal and provincial (many scientists in the field feel
| threatened by ML and automation; others just don't know how
| to work in a large-scale environment, others want to come
| up with the perfect experiment yet never actually run one,
| and even others want to use the automater as a quick-turn-
| around, not economy-of-scale tool. Further, just getting
| the necessary support infra to make the system run well can
| often be quite challenging.
| bee_rider wrote:
| How much do a small number of well trained chemists cost to
| employ? I'd expect north of $100k a pop. Although I guess
| this doesn't help if you still need a chemist to interpret
| the output of the arm.
| timdellinger wrote:
| Indeed, the x-ray diffraction interpretation wasn't
| completely automated. From the experimental paper: "When
| the automated refinement gives a poor fit, manual analysis
| is performed"
| petsfed wrote:
| We already do this with automated e.g. drug testing. I've
| worked with a couple different machines (and worked on the
| development of another) that existed specifically to rapidly
| do certain chemical and biological tests, to parse through
| computationally or AI suggested drugs. They run at about the
| same cost, with similar service contracts, and they're VERY
| common in the pharmaceutical industry. If your goal was to
| find a process to produce some precursor chemical necessary
| for material development (prior to heading to the foundry),
| it makes sense.
| dekhn wrote:
| I work in pharma and most of the time when I visit labs,
| they don't run 24/7 and in fact run at about 10% or less of
| their total capacity.
| petsfed wrote:
| Well, people are still buying them, otherwise I'd be out
| of job. My experience with the Tecan and Hamilton
| machines that I got to interact with was that the setup
| seemed tedious as hell, but once it was off and running,
| it would rapidly outpace even the best pipetters.
| dekhn wrote:
| yeah they are great for employment insurance.
| tcpekin wrote:
| Grad students at Cal cost a professor ~100k a year, and then
| leave after 2-5 years with any optimizations they might have
| personally made. They also only work 6-12 hours a day, and
| having been said grad student, get mind numbingly bored after
| about 10-15 repetitive syntheses, spending lots of time on
| them, when the (only) interesting part, is the XRD pattern at
| the end... I would have absolutely advocated for such an arm
| if I was still there.
| happydog wrote:
| I don't think identification of possible new materials is a rate-
| limiting step for discovery of better catalysts, batteries, etc.
| The problem is not coming up with new materials -- it's coming up
| with new materials that _have desired properties_ and _can be
| cheaply synthesized_.
|
| It's like if you asked a chemist to draw a few possible
| structures for organic molecules that have never been
| synthesized. They can do that. But not all of those possible
| molecules they came up with will be easy to synthesize. And
| neither they nor anyone else (without doing a lot of experimental
| work) will be able to tell you which of those possible
| structures, if any, would work as a painkiller or an oncology
| drug.
|
| Still, I do think this is a nice demonstration of how more data
| enables very accurate predictions of energies that would
| otherwise require expensive DFT calculations. That part is
| definitely interesting.
| aabhay wrote:
| 100% agreed. This is primarily a breakthrough on using graph
| networks to show some promise on the task. It will take several
| more iterations for it to be transformative to the industry.
| jacoblambda wrote:
| I think a more interesting application of this process is to
| attempt to find easier, safer, more reliable, or more efficient
| methods of producing or processing existing materials.
|
| See: the more or less accidental rediscovery of room
| temperature polyester/PET recycling (including separation from
| blended fabrics without damaging the cotton) using CO2 as a
| catalyst.
|
| There exist quite a few cases of very simple solutions to very
| difficult problems where the start and end products are already
| known, but we just don't know how to effectively get from A to
| B without causing certain undesirable side-effects.
| dekhn wrote:
| I'm certain you could build an embedding that provided a
| utility function for molecules based on price and
| synthesizability. That's an approximation of what the chemist's
| brain is doing.
|
| You wouldn't ask a chemist to evaluate the molecules (in drug
| discovery), though- you'd have a molecular biologist (really a
| lab tech) set up a screening campaign, and in many cases, the
| biological readout that predicts something could work as a
| painkiller or oncology drug is relatively straightforward to
| implement experimentally at scale (high throughput screening).
| Unfortunately those readouts aren't super-predictive of the
| full biology, however.
|
| I expect DeepMind or Isomorphic to announce, in the next five
| years, that they have made a model that can quickly identify
| whether a specific molecule would be likely to pass clinical
| trials and the rest of the FDA process. With a false negative
| rate ("predict that a drug would not get through to approval,
| but in reality it would have") below around 25%, we could
| easily save billions a year in failed drug costs.
| dpflan wrote:
| Isn't the problem how to actually scale these discoveries to
| industry processes? Yes you can create some crazy materials a low
| levels, but scaling up the small scale stable processes is
| difficult.
| whatever1 wrote:
| Nice trick, but it's almost useless.
|
| We have been using for decades integer programming to explore all
| the possible permutations with hard constraints that include
| manufacturability.
|
| Their references list is lacking, to say the least.
| timdellinger wrote:
| For all the automation effort, there's always something that has
| to be done by hand...
|
| From the experimental paper: "The XRD sample holders must be
| cleaned manually when the lab has depleted its stock"
| timdellinger wrote:
| A couple other observations on the experimental side:
|
| They define success as being a sample with >50% of the target
| material. I guess that's success, but wow you can't test any
| actual properties (hardness, electrical conductivity, etc.)
| with samples like that.
|
| As the reviewers noted, they're only making oxides (no alloys
| or intermetallics).
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