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Aron Walsh, a materials scientist at Imperial College London who was not involved in the research, says DeepMind’s work is impressive.
没有参与这项研究的伦敦帝国理工学院材料科学家阿隆·沃尔什表示，深度思维的成果令人印象深刻。
But “this is the start of the exploration rather than the end,” he says, noting that the machine has only scratched the surface of what might be possible.
但“这是探索的开始，而不是结束，”他说，并指出这台机器只触及了所有可能的表面。
In a recent paper of his own he tried to calculate how many stable crystals incorporating four chemical elements (so-called quaternaries) might be potentially manufacturable.
在他自己最近的一篇论文中，他试图计算有多少含有四种化学元素的稳定晶体(所谓的季盐化合物)可以被制造出来。
He wound up with a conservative estimate of 32trn.
他最后得出的保守估计是32万亿。
For its part, GNOME looked only at crystals that form under relatively low temperatures and pressures.
就GNOME而言，它只研究了在相对较低的温度和压力下形成的晶体。
And crystals are only one subset of a universe of materials that includes everything from amorphous solids such as glass through to gases, gels and liquids.
晶体只是物质宇宙的一个子集，包括从无定形固体(如玻璃)到气体、凝胶和液体的所有物质。
Whether any of DeepMind’s 2.2m new crystals will be useful remains to be seen.
深度思维的220万个新晶体中是否存在有用晶体还有待观察。
Even if they do not, the techniques used to make the predictions could be valuable.
即使其中没有，用于预测的技术也可能是有价值的。
Besides suggesting new crystals, AI may also shed light on as-yet-unknown rules that govern how they form.
除了提出新晶体外，人工智能还可能揭示控制它们如何形成的未知规则。
Ekin Dogus Cubuk at DeepMind highlights one such finding.
深度思维的Ekin Dogus Cubuk强调了其中一个发现。
Previously, he says, crystals made from six elements, called senaries, were thought to be vanishingly rare.
他说，以前，由六种元素组成的晶体（被称为六盐化合物）被认为是非常罕见的。
But DeepMind’s ai found around 3,200 in its sample of 381,000 stable compounds.
但深度思维的人工智能在38.1万种稳定化合物的样本中发现了大约3200种这类化合物。
A better understanding of how crystals form, and what sorts are possible, might also save scientists curious to test how the 2.2m new materials behave from the tedious task of synthesising each one of them by hand.
更好地了解晶体如何形成，以及可能形成的种类，也可以使科学家们从手工合成每一种新材料的繁琐任务中解脱出来，去测试220万种新材料的性能。
