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Discovery consists of seeing what everybody has seen and thinking what nobody has thought.(发现就是看到每个人都看到的，思考没有人想到的。)

早上好，读者朋友们，今天分享的文章选自《经济学人》。探索科学的新前沿，Google AI引领发现2.2万种未知材料！近日，Google的人工智能系统在物质科学领域掀起了一场革命。它成功地发现了220万种前所未有的新材料，这些材料在科学界尚属未知。这些新材料的特性、潜在用途以及它们可能带来的影响令人充满好奇。让我们一起跟随这篇文章，探索这个激动人心的发现，了解这些新材料如何可能改变我们的世界，引领我们进入一个全新的科技时代。

A Google AI has discovered 2.2m materials unknown to science

谷歌人工智能发现了220万种科学未知材料

Crystals can do all sorts of things, some more useful than others. But they can also serve as the light-harvesting layer in a solar panel, catalyse industrial reactions to make things like ammonia and nitric acid, and form the silicon used in microchips. That diversity arises from the fact that “crystal” refers to a huge family of compounds, united only by having an atomic structure made of repeating units—the 3d equivalent of tessellating tiles.

crystal   n.晶体；结晶

ammonia   n.氨

compound   n.复合物；混合物

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晶体可以做各种各样的事情，有些比其他更有用。但它们也可以作为太阳能电池板的光收集层，催化工业反应以制造氨和硝酸等物质，并形成用于微芯片的硅。这种多样性源于这样一个事实，即“晶体”指的是一个庞大的化合物家族，只有由重复单元组成的原子结构才能将其结合起来——相当于镶嵌瓷砖的三维结构。

Just how huge is highlighted by a paper published in Nature by Google DeepMind, an artificial-intelligence company. Scientists know of about 48,000 different crystals, each with a different chemical recipe. DeepMind has created a machine-learning tool called gnome (Graph Networks for Materials Exploration) that can use existing libraries of chemical structures to predict new ones. It came up with 2.2m crystal structures, each new to science.

highlighte   v.突出，强调

recipe   n.食谱；方法，秘诀

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人工智能公司Google DeepMind在《自然》杂志上发表的一篇论文强调了人工智能的巨大程度。科学家们知道大约有48000种不同的晶体，每种晶体都有不同的化学配方。DeepMind创建了一个名为gnome(图网络材料探索)的机器学习工具，可以使用现有的化学结构库来预测新的化学结构。它发现了220万个晶体结构，每一个都是科学上的新发现。

To help any other laboratories keen to investigate the computer’s bounty, the firm has made public a subset of what they think should be the 381,000 most stable structures. Among them are many thousands of crystals with structures potentially amenable to superconductivity, in which electrical currents flow with zero resistance, and several hundred potential conductors of lithium ions that could find a use in batteries. In both cases DeepMind’s work has increased the total number of candidate materials known to researchers tens of times over.

keen   adj.热衷于

bounty   n.奖金；慷慨之举

resistance  n.反对，抵制；电阻

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为了帮助其他热衷于研究这台计算机的实验室，该公司公开了他们认为应该是381,000个最稳定结构的子集。其中包括数千种具有潜在超导结构的晶体，其中电流以零电阻流动，以及数百种可能用于电池的锂离子导体。在这两种情况下，DeepMind的工作都使研究人员已知的候选材料总数增加了数十倍。

Whether any of DeepMind’s 2.2m new crystals will be useful remains to be seen. Even if they do not, the techniques used to make the predictions could be valuable. Besides suggesting new crystals, AI may also shed light on as-yet-unknown rules that govern how they form.Ekin Dogus Cubuk at DeepMind highlights one such finding. Previously, he says, crystals made from six elements, called senaries, were thought to be vanishingly rare. But DeepMind’s ai found around 3,200 in its sample of 381,000 stable compounds. 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. 

shed light on   启发，启示

sample   n. 样本，样品

synthesize   v.合成

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DeepMind的220万个新晶体中是否有有用的还有待观察。即使他们没有，用于预测的技术也可能是有价值的。除了提出新的晶体外，人工智能还可能揭示支配它们如何形成的未知规则。DeepMind的Ekin Dogus Cubuk强调了其中一个发现。他说，以前，由六种元素组成的晶体，被称为七元素，被认为是非常罕见的。但DeepMind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。对于晶体如何形成以及可能存在哪些种类的更好的理解，也可能会使那些想要测试这220万种新材料行为的科学家们从繁琐的手工合成任务中解脱出来。

长难句解析

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. 

句子主干是：A better  understanding of … might also save…   .of how crystals form, and what sorts are possible是介词短语作定语，修饰主语A better understanding，curious to test how the 2.2m new materials behave是不定式短语作形容词短语，作为定语修饰宾语scientists，表示“好奇于测试这220万种新材料行为的科学家”.

翻译：对于晶体如何形成以及可能存在哪些种类的更好的理解，也可能会使那些想要测试这220万种新材料行为的科学家们从繁琐的手工合成任务中解脱出来。

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