在生物学中，, 缺陷通常是不好的。但在材料科学中，, 缺陷可以有意调整，从而赋予材料有用的新特性。如今，, 原子级缺陷在钢, 半导体, 和太阳能电池等产品的制造过程中被小心引入，以帮助提高强度, 控制导电率, 优化性能, 等。
In biology, defects are generally bad. But in materials science, defects can be intentionally tuned to give materials useful new properties. Today, atomic-scale defects are carefully introduced during the manufacturing process of products like steel, semiconductors, and solar cells to help improve strength, control electrical conductivity, optimize performance, and more.
但即使缺陷已成为一种强大的工具,，准确测量不同类型的缺陷及其在成品中的浓度仍然具有挑战性,，尤其是在不切开或损坏最终材料的情况下。如果不知道他们的材料存在哪些缺陷，, 工程师就有可能制造出性能不佳或具有意外特性的产品。
But even as defects have become a powerful tool, accurately measuring different types of defects and their concentrations in finished products has been challenging, especially without cutting open or damaging the final material. Without knowing what defects are in their materials, engineers risk making products that perform poorly or have unintended properties.
现在, 麻省理工学院的研究人员已经建立了一个人工智能模型，能够使用非侵入性中子散射技术的数据对某些缺陷进行分类和量化。在 2,000 种不同的半导体材料, 上训练的模型, 可以同时检测材料中多达六种点缺陷,，这是单独使用传统技术不可能实现的。
Now, MIT researchers have built an AI model capable of classifying and quantifying certain defects using data from a noninvasive neutron-scattering technique. The model, which was trained on 2,000 different semiconductor materials, can detect up to six kinds of point defects in a material simultaneously, something that would be impossible using conventional techniques alone.
研究人员表示，该模型是朝着更精确地利用半导体,微电子,太阳能电池,和电池材料等产品中的缺陷迈出的一步。
The researchers say the model is a step toward harnessing defects more precisely in products like semiconductors, microelectronics, solar cells, and battery materials.
“现在,检测缺陷就像俗话说看到大象:每种技术只能看到它的一部分,”高级作者、核科学与工程副教授李明达说。 “有些看到鼻子,其他看到躯干或耳朵。但要看到完整的大象却非常困难。我们需要更好的方法来全面了解缺陷,，因为我们必须了解它们才能使材料更有用。”
“Right now, detecting defects is like the saying about seeing an elephant: Each technique can only see part of it,” says senior author and associate professor of nuclear science and engineering Mingda Li. “Some see the nose, others the trunk or ears. But it is extremely hard to see the full elephant. We need better ways of getting the full picture of defects, because we have to understand them to make materials more useful.”
制造商已经擅长调整材料中的缺陷,，但测量成品中缺陷的精确数量在很大程度上仍然是一个猜谜游戏。
Manufacturers have gotten good at tuning defects in their materials, but measuring precise quantities of defects in finished products is still largely a guessing game.
“工程师有很多方法来引入缺陷,，例如通过掺杂,，但他们仍然在努力解决基本问题，例如他们’创造了什么样的缺陷以及以什么浓度,” Fu说。 “有时它们也有不需要的缺陷,，例如氧化。他们’t总是知道他们是否在合成过程中引入了一些不需要的缺陷或杂质。这是一个长期存在的挑战。’。”
“Engineers have many ways to introduce defects, like through doping, but they still struggle with basic questions like what kind of defect they’ve created and in what concentration,” Fu says. “Sometimes they also have unwanted defects, like oxidation. They don’t always know if they introduced some unwanted defects or impurity during synthesis. It的 a longstanding challenge.”
结果是每种材料往往存在多个缺陷。不幸的是, 每种理解缺陷的方法都有其局限性。 X 射线衍射和正电子湮灭等技术只能表征某些类型的缺陷。拉曼光谱可以辨别缺陷类型，但可以’t直接推断浓度。另一种称为透射电子显微镜的技术需要人们将样品切成薄片进行扫描。
The result is that there are often multiple defects in each material. Unfortunately, each method for understanding defects has its limits. Techniques like X-ray diffraction and positron annihilation characterize only some types of defects. Raman spectroscopy can discern the type of defect but can’t directly infer the concentration. Another technique known as transmission electron microscope requires people to cut thin slices of samples for scanning.
在之前的几篇论文中，, Li 和合作者将机器学习应用于实验光谱数据来表征晶体材料。对于新论文,，他们希望将该技术应用于缺陷。
In a few previous papers, Li and collaborators applied machine learning to experimental spectroscopy data to characterize crystalline materials. For the new paper, they wanted to apply that technique to defects.
对于他们的实验,，研究人员建立了一个 2,000 半导体材料的计算数据库。他们制作了每种材料, 的样品对，其中一种掺杂了缺陷，另一种则没有缺陷,，然后使用中子散射技术测量固体材料中原子的不同振动频率。他们根据结果训练了机器学习模型。
For their experiment, the researchers built a computational database of 2,000 semiconductor materials. They made sample pairs of each material, with one doped for defects and one left without defects, then used a neutron-scattering technique that measures the different vibrational frequencies of atoms in solid materials. They trained a machine-learning model on the results.
“建立了一个涵盖元素周期表中 56 个元素的基础模型,” Cheng 说。 “该模型利用多头注意力机制,，就像 ChatGPT 使用的一样。它同样提取有缺陷和没有缺陷的材料之间的数据差异，并输出对使用的掺杂剂和浓度的预测。”
“That built a foundational model that covers 56 elements in the periodic table,” Cheng says. “The model leverages the multihead attention mechanism, just like what ChatGPT is using. It similarly extracts the difference in the data between materials with and without defects and outputs a prediction of what dopants were used and in what concentrations.”
研究人员对他们的模型, 进行了微调，并在实验数据, 上进行了验证，并表明它可以测量电子器件中常用的合金和单独的超导材料中的缺陷浓度。
The researchers fine-tuned their model, verified it on experimental data, and showed it could measure defect concentrations in an alloy commonly used in electronics and in a separate superconductor material.
研究人员还多次掺杂材料以引入多个点缺陷并测试模型, 的极限，最终发现它可以同时预测材料中最多六个缺陷,，且缺陷浓度低至 0.2%。
The researchers also doped the materials multiple times to introduce multiple point defects and test the limits of the model, ultimately finding it can make predictions about up to six defects in materials simultaneously, with defect concentrations as low as 0.2 percent.
“我们真的很惊讶它的效果这么好,” Cheng 说。 “It 对两种不同类型缺陷的混合信号进行解码非常具有挑战性 — 更不用说六个了。”
“We were really surprised it worked that well,” Cheng says. “It的 very challenging to decode the mixed signals from two different types of defects — let alone six.”
通常，, 半导体等产品的制造商会在一小部分产品从生产线下线时对其进行侵入式测试,，这是一个缓慢的过程，限制了他们检测每个缺陷的能力。
Typically, manufacturers of things like semiconductors run invasive tests on a small percentage of products as they come off the manufacturing line, a slow process that limits their ability to detect every defect.
“现在,人们主要估计其材料中的缺陷数量,” Yu说。 “使用每种单独的技术检查估计值是一次艰苦的经历,无论如何只能提供单一颗粒的本地信息。它会对人们认为自己的材料存在哪些缺陷产生误解。”
“Right now, people largely estimate the quantities of defects in their materials,” Yu says. “It is a painstaking experience to check the estimates by using each individual technique, which only offers local information in a single grain anyway. It creates misunderstandings about what defects people think they have in their material.”
结果让研究人员感到兴奋,，但他们指出，他们用中子测量振动频率的技术对于公司来说很难在自己的质量控制流程中快速部署。
The results were exciting for the researchers, but they note their technique measuring the vibrational frequencies with neutrons would be difficult for companies to quickly deploy in their own quality-control processes.
“此方法非常强大,，但其可用性有限,” Rha 说。 “振动谱是一个简单的想法,，但在某些设置中它’非常复杂。有一些基于其他方法,（例如拉曼光谱,）的更简单的实验设置可以更快地采用。”
“This method is very powerful, but its availability is limited,” Rha says. “Vibrational spectra is a simple idea, but in certain setups it的 very complicated. There are some simpler experimental setups based on other approaches, like Raman spectroscopy, that could be more quickly adopted.”
Li 表示，公司已经对这种方法表示了兴趣，并询问何时将其与拉曼光谱,（一种广泛使用的测量光散射的技术）结合使用。 Li 表示，研究人员 下一步是根据拉曼光谱数据训练类似的模型。他们还计划扩展他们的方法来检测大于点缺陷, 的特征，如晶粒和位错。
Li says companies have already expressed interest in the approach and asked when it will work with Raman spectroscopy, a widely used technique that measures the scattering of light. Li says the researchers next step is training a similar model based on Raman spectroscopy data. They also plan to expand their approach to detect features that are larger than point defects, like grains and dislocations.
目前,虽然,，研究人员相信他们的研究证明了人工智能技术在解释缺陷数据方面的固有优势。
For now, though, the researchers believe their study demonstrates the inherent advantage of AI techniques for interpreting defect data.
“对于人眼,这些缺陷信号看起来基本相同,” Li 说。 “但是人工智能的模式识别能力足以辨别不同的信号并得出真相。缺陷是把双刃剑。有很多好的缺陷,，但如果, 太多，性能可能会下降。这开辟了缺陷科学的新范式。”
“To the human eye, these defect signals would look essentially the same,” Li says. “But the pattern recognition of AI is good enough to discern different signals and get to the ground truth. Defects are this double-edged sword. There are many good defects, but if there are too many, performance can degrade. This opens up a new paradigm in defect science.”