Materials
As well as discovering millions of new crystals, DeepMind is releasing the predicted structures of 380,000 materials.
– the researchers compare it to the connection between atoms – GNNs are ideal for discovering new crystalline materials. For their part, candidates for research are taken from something called the Materials Project, an open-source database encompassing around 35,000 molecules and over 130,000 inorganic compounds. From there, Cubuk takes up the story of how his platform actually works. “We used a training process called ‘active learning’ that dramatically boosted GNoME’s performance,” he explains. “GNoME would generate predictions for the structures of novel, stable crystals – which were then tested using DFT. The resulting high-quality training data was then fed back into our model training.” Examine GNoME’s results, meanwhile, and it’s clear this approach is doing well. As Cubuk says, by scaling up GNN training, it’s possible to boost the platform’s “discovery efficiency” from just a few percent to over 80%. That’s bearing fruit in other ways too. Beyond discovering over two million new crystals, Cubuk and his colleague Amil Merchant are also releasing the predicted structures of the 380,000 materials they feel have the best chance of being replicated in the lab.
736
The number of GNoME materials external researchers have successfully replicated in the lab.
Google DeepMind 98
It goes without saying, of course, that success with GNoME isn’t an end in itself. Rather, the idea is to use its theoretical discoveries to then transform science in the real world. Fortunately, there are signs that here too, the DeepMind team are prodding their sector forward, with researchers already creating 736 of GNoME’s new materials in the lab. Just as important, some of these creations show real promise. Take, for example, a compound called Li4MgGe2S7, an alkaline-earth material that shows huge promise as an optical material. Mo5GeB2 on the other hand, is a potential superconductor. Considering the need for exactly such materials right across medical life, from beam therapy to diagnostic imaging, that’s doubtless good news.
Given these achievements, should we expect a flood of exciting new materials imminently? Perhaps not. As
Cubuk warns, one of the fundamental issues involves the machine learning (ML) that underpins GNoME. “One challenge,” he says, “was in being able to create a large enough training set while also preserving the amount of ‘novelty’ in the training samples. This is in general an unsolved problem in deep learning: we don’t know how to quantify the diversity of a training set sample in an automated fashion.” To be fair, this stumbling block is hardly insurmountable. By training various GNN to predict the energy of a new sample, then comparing how much the various GNN disagreed, the experts were able to estimate a sample’s novelty. A more pressing hurdle, rather, is securing the manufacturing capabilities needed to get new wonder materials into the hands of doctors. As Cubuk says: “Computational tools and predictions do not mean much unless they impact the actual materials being developed in the lab. This has been our goal from the beginning, but it takes a lot of time and research, and we are still working on it.” Fair enough: while it’s obviously possible to simulate hundreds of thousands of materials at once, actually experimenting on them using physical space is a wholly different matter. Yet here too, there are signs that progress is imminent. After partnering with DeepMind, to give one example, the Lawrence Berkeley National Laboratory successfully synthesised more than 40 new materials. Tellingly – and just like GNoME generally – this practical work is being prodded along with the help of new technology. Rather than relying on flesh-and-blood researchers, after all, the Berkeley lab instead programmed robots to do the work, presaging a future where autonomous labs could build on the victories of AI scientists. Certainly, Cubuk seems to be looking this way himself. “The next goal,” as he puts it, “is to use DFT and ML to predict and influence experiments.” An exciting prospect, and surely one William David Coolidge would have admired. ●
Medical Device Developments /
www.nsmedicaldevices.com
Google DeepMind
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