Materials
Discovering materials faster and more intentionally can help unlock future tech that is ‘critical’ for devices.
From these unsure beginnings, so-called ‘ductile tungsten’ filaments would go on to dominate lightbulbs for a century, even as 795 million were sold in 1945 alone. The point, at any rate, is that many of the great scientific discoveries of earlier times rested on tungsten falling into mercury. They rested, in other words, on pure luck. But what if scientists could remove the trial and error from their research? What if, instead, they could use powerful algorithms to discover thousands of viable materials? As a team of researchers at Google DeepMind are vividly proving, both these revolutions could soon become reality – especially when dovetailed by similar advances in manufacturing labs.
Trials and errors
Discovering new materials is central to scientific development. That’s clear enough in the tungsten example – but also in the medical device space. Consider, for instance, the rise of so-called nanomaterials, and how something like nanosilver can be used to prevent or reduce inflammation. Humbler materials doubtless have a role to play here too. Robust kinds of synthetic rubber can, for instance, be used to develop oncology drug devices. Lightweight and heat-resistant, borosilicate glass wafers are central to machines like x-rays, even as novel alloys like nitinol are proving useful as mouldable stents. “Every single technology is enabled and limited by the materials that are involved,” says Ekin Dogus Cubuk, a research scientist at Google DeepMind. “Being able to discover and develop materials faster and more intentionally can help improve current technologies and unlock future technologies that are critical for medical devices.” If you examine the numbers, it’s hard to disagree. According to work by Precedence Research, for example, the global advanced materials market was already worth some $61bn in 2022, a figure expected to reach over $112bn by 2032. Listen to Cubuk, however, and it’s obvious that much of the progress here has
Medical Device Developments /
www.nsmedicaldevices.com
traditionally been painstaking. As he stresses: “Many materials discoveries involve a lot of trial and error, luck and serendipity.” That’s apparent enough far beyond lightbulbs, with everything from implantable pacemakers to coronary angiograms all accidental finds. It goes without saying that – in principle anyway – the immense power of computers offers a solution here. Given, after all, that AI can now sift through millions of theoretical crystal structures at speed, then predict the most viable for a range of medical uses, medical science should by rights be on the verge of a revolution. Yet if the worldwide materials informatics is set to enjoy CAGR of 13.7% through 2030, bringing it to over $702m, Cubuk equally warns that computational approaches to material design have traditionally been beset by problems.
That begins, Cubuk explains, with something called ‘density functional theory’ (DFT). A computational quantum mechanical modelling method for simulations in materials, it struggles to simulate larger and more complex structures. No less important, the researcher continues, modelling materials at short time frames can be prohibitively expensive for DFT. “Similarly,” he adds, “DFT is not necessarily suitable for predicting materials with quantum properties or more complex physics under increasing temperature.” Because of their unusual properties, including superconductivity, so-called quantum materials could soon transform medical life. The fact that existing algorithms can’t truly understand them is therefore an obvious difficulty, not least when international private investment into quantum technology has topped $7bn since 2012.
Going deep
Into this exciting if underdeveloped field steps Cubuk. Together with a colleague at Google DeepMind, he’s developed Graph Networks for Materials Exploration (GNoME’), a state-of-the-art graph neural network (GNN) model. Referring to the form the input data takes
$112bn
The estimated size of the global advanced materials market by 2032.
Precedence Research 97
nevodka/
Shutterstock.com
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