New materials designed by artificial intelligence promise to provide stronger hip replacements and improve how fractures healing.
由人工智能设计的新材料有望提供更坚固的髋关节置换植入物，并改善骨折愈合方式。
A few years ago, Amir Zadpoor was searching for a peculiar new material.
几年前，阿米尔·扎德普尔正在寻找一种奇特的新材料。
He needed one that would get thicker when stretched, yet also be stiff like bones.
他需要一种在被拉伸时会变厚、但同时又像骨头一样坚硬的材料。
It was quite an ask.
这可是一个相当高的要求。
Think about what happens when you pull an elastic band from both ends – as it elongates, the elastic gets ever thinner.
想想当你从两端拉一根橡皮筋时会发生什么：随着它被拉长，橡皮会变得越来越细。
Zadpoor, a professor of orthopaedics at the Leiden University Medical Center in the Netherlands, and his team needed something that would doesn't the exact opposite.
扎德普尔是荷兰莱顿大学医学中心的骨科教授，他和团队需要一种能产生完全相反效果的东西。
It would almost need to defy the laws of physics.
它几乎需要违背物理定律。
The problem they were facing were hips.
他们当时面对的问题是髋关节。
Hip replacements are one of the most common orthopaedic procedures conducted around the world.
髋关节置换是全球最常见的骨科手术之一。
The problem is that people with artificial hips take around two million steps per year, which subjects an implant to forces that gradually wear it down.
问题在于，装有人工髋关节的人每年大约要走两百万步，这会使植入物承受各种力，并逐渐被磨损。
After a decade or more of Using, implants often need to be replaced.
使用十年或更久之后，植入物往往需要更换。
Zadpoor and his colleagues hoped to solve this problem by putting two different materials that behave in opposite ways when stretched on either side of an implant's base – one that becomes thicker when compressed and the other that would thicken when stretched.
扎德普尔和他的同事们希望通过在植入物基底两侧放置两种拉伸时表现相反的材料来解决这个问题：一种在受压时会变厚，另一种在被拉伸时会变厚。
This would help to cushion the femur when the joint was under pressure and ensure implant stay fixed snugly against the bone.
这将有助于在关节承受压力时缓冲股骨，并确保植入物紧贴骨头牢固固定。
"That would reinforce the connection between the bone and the implant," says Zadpoor.
“那会加强骨骼与植入物之间的连接，”扎德普尔说。
All their research had suggested it would work.
他们所有的研究都表明，这种方法会奏效。
Only there was another catch – the few known materials that become thicker when stretched, called auxetic materials, tend to be softer and compliant.
只是还有另一个问题：少数已知会在拉伸时变厚的材料，也就是所谓的拉胀材料，往往柔软且顺从变形。
They are used in crash helmets and knee pads, for example.
例如，它们被用于防撞头盔和护膝。
"We were trying to finding this holy grail of auxeticity and also a high stiffness to be able to carry the loads," says Zadpoor.
扎德普尔说：“我们试图找到这种既具有负泊松比特性、又具有高刚度以承受载荷的‘圣杯’。”
"That becomes a formidable hunt."
“这就变成了一场艰巨的搜寻。”
The team turned to artificial intelligence for help.
这个团队转而求助于人工智能。
Using an AI system trained to predict how different materials might behave, they were able to plug in the specific properties they desired.
他们使用一个经过训练、能够预测不同材料可能如何表现的 AI 系统，输入了自己想要的具体性质。
The machine came back with a design for something known as a "metamaterial" – materials that could be engineered to have bizarre properties by altering their microscopic structure.
机器给出了一个所谓“超材料”的设计，也就是可以通过改变微观结构而被工程化设计出奇特性质的材料。
Their work is just one example of how scientists are increasingly turning to AI to help them dream up materials that would have once been inconceivable.
他们的工作只是一个例子，说明科学家正越来越多地借助人工智能，帮助他们构想出过去曾经难以想象的材料。
And it is proving to be particularly powerful for those trying to mimic the properties of biological tissues.
事实证明，对于那些试图模仿生物组织特性的人来说，它尤其强大。
"With machine learning, you can make (the process) orders of magnitude faster and that allows you to explore thousands to millions of more structures to find what you need," says Zadpoor.
扎德普尔说：“借助机器学习，你可以让这个过程快上好几个数量级，从而能够探索多出成千上万乃至数百万种结构，找到你需要的东西。”
Metamaterials can be designed to have an array of properties, depending on their internal structure – they can behave either as a solid or a liquid depending on a specific frequency of sound applied to them, for example.
超材料可以根据其内部结构被设计成具备一系列特性，例如，根据施加在它们身上的特定声音频率，它们既可以表现得像固体，也可以表现得像液体。
But finding what internal structure might giving rise to desired properties is still a challenge when relying on physics-based methods or simulations.
但是，如果依靠基于物理的方法或模拟，找出哪种内部结构可能产生所需特性仍然是一项挑战。
It can take about a year to develop and train an AI model to generate new material designs, says Sid Kumar, an associate professor of materials science TU Delft in the Netherlands.
荷兰代尔夫特理工大学材料科学副教授西德·库马尔说，开发并训练一个用于生成新材料设计的AI模型可能需要大约一年时间。
But once that is in place, it can take minutes or even seconds for the system to generate feasible designs.
但一旦模型就绪，系统可能只需几分钟，甚至几秒钟，就能生成可行的设计。
In one of their projects, Kumar and his colleagues used AI to come up with a metamaterial that could be used to create soft bone implants to repair complex fractures, which are common in elderly people.
在他们的一个项目中，库马尔和同事利用 AI 设计出一种超材料，可用于制造柔软的骨植入物，以修复老年人中常见的复杂骨折。
Plates, screw and rods made of titanium or steel are often used currently, but bone doesn't always heal well around them.
目前常使用由钛或钢制成的钢板、螺钉和杆，但骨头在这些材料周围并不总能很好地愈合。
This can mean these implants do not integrate properly, leaving them weak.
这可能意味着这些植入物无法与骨头很好地融合，从而变得不牢固。
The researchers thought that a softer material that can still provide structure might better mimic the soft tissue that naturally forms in the early stages of healing in a fracture.
研究人员认为，一种较软但仍能提供结构支撑的材料，或许能更好地模拟骨折愈合早期自然形成的软组织。
They wanted a metamaterial that incorporates a lattice-like microstructure but also has liquid-like properties like a polymer or hydrogel.
他们想要一种超材料，既包含类似晶格的微观结构，又像聚合物或水凝胶一样具有类液体特性。
This soft material, which could be designed to look like a thin, circular bandage containing holes, would be placed on a fracture so that living cells can colonise it and allow it to integrate with the bone.
这种柔软材料可以被设计成带有孔洞的薄圆形绷带状，并被放置在骨折处，让活细胞能够在其上定植，使它与骨头融为一体。
"The early stage of fracture healing is decisive for success," says Xiao-Hua Qin, an assistant professor of biomaterial engineering at ETH Zurich in Switzerland and a member of the research team.
“骨折愈合的早期阶段对成功至关重要，”研究团队成员、瑞士苏黎世联邦理工学院生物材料工程助理教授秦晓华说。
Metal implants used to repair fractures are also more resilient than bone, which can be problematic since they absorb external forces.
用于修复骨折的金属植入物也比骨骼更有韧性，而这可能会带来问题，因为它们会吸收外部力量。
The bone that forms around them therefore doesn't experience strain during exercise and so can start to die.
因此，在这些植入物周围形成的骨头在运动时不会承受应变，于是可能开始坏死。
Kumar and his colleagues therefore also wanted a metamaterial that had the same shape and properties as can be found at the joint ends of longer bones, for example those in our arms and legs.
因此，库马尔和他的同事还希望找到一种超材料，它具有与长骨关节末端相同的形状和性质，例如我们手臂和腿部骨骼末端的那种结构。
Here, the internal bone has a porous and honeycomb-like structure, known as trabecular bone, that provides strength and the ability to absorb shocks.
在这里，骨骼内部呈多孔、蜂窝状结构，被称为小梁骨，它提供强度以及吸收冲击的能力。
In previous work, Kumar and his colleagues had introduced a new class of metamaterials, called spinodoids, that share several important features with porous bone.
在此前的研究中，库马尔和他的同事提出了一类新的超材料，称为spinodoids，它们与多孔骨具有若干重要共同特征。
Both have lattice-like internal structures that are slightly irregular in shape.
两者都具有类似晶格的内部结构，而且形状略微不规则。
Depending on how these are orientated, they create varying levels of strength and stiffness.
根据这些结构的取向不同，它们会产生不同程度的强度和刚度。
By giving a machine learning model algorithim a list of the properties they were after, such as the specific stiffness of a femur bone, Kumar and his colleagues were able to generate spinodoid designs that closely matched human bone.
通过向机器学习模型算法提供他们想要的属性清单，例如股骨的特定刚度，Kumar和同事们得以生成与人体骨骼高度匹配的旋节线结构设计。
They were able to mimic its curvature as well as its porous inner structure, for example, as well as how it behaves when a force is applied to it.
例如，他们能够模仿它的曲率和多孔的内部结构，也能模仿它在受力时的表现。
"This is important because you may want one region of the implant to be stiffer, another region to be more porous and another region to encourage tissue ingrowth," says Mohammad Mirzaali, an associate professor of biomedical engineering at TU Delft, who was not involved with the work.
未参与这项工作的代尔夫特理工大学生物医学工程副教授穆罕默德·米尔扎阿里说：“这一点很重要，因为你可能希望植入物的某个区域更硬，另一个区域更具多孔性，还有一个区域能够促进组织向内生长。”
Kumar and his team were also able to shown that the design could be fabricated using three dimensional printing techniques.
库马尔和他的团队还证明，这种设计可以利用三维打印技术制造出来。
Their next step is to do tests to figure out how it would fare if implanted in the human body.
他们的下一步是进行测试，以弄清楚如果将其植入人体，它会表现如何。
"Maybe a few years down the line, we'll be able to make mimetic bone implants," says Kumar.
“也许几年后，我们就能制造仿生骨植入物，”库马尔说。
Zadpoor and his colleagues have also moved forward with their quest to find an improbable metamaterial for hip implants.
扎德普尔和他的同事们也在寻找一种用于髋关节植入物的看似不可能的超材料这一探索上取得了进展。
They have added to their list of properties – durable enough to long-lasting under stress and tuneable to fit into the variable space of a patient's hip.
他们在所需属性清单中又加入了几项要求：要足够耐用，能在压力下长期保持性能，并且可调节，以适应患者髋部多变的空间结构。
To satisfy their long wish list, Zadpoor and his team got three different machine learning models to join forces and hunt for a feasible metamaterial together.
为了满足这份很长的愿望清单，Zadpoor和他的团队让三种不同的机器学习模型联手，共同寻找一种可行的超材料。
The approach resulted in several auxetic metamaterial designs that would be suitable for use in a bone implant, which Zadpoor says would be impossible to achieve without AI due to the complexity of the task.
这种方法产生了几种适合用于骨植入物的拉胀超材料设计；扎德普尔表示，由于任务复杂，如果没有 AI，这将不可能实现。
In the future, machine learning may even make it possible to tailor individual bone implants to a patient's anatomy, which should make it last longer, says Zadpoor.
Zadpoor说，未来，机器学习甚至可能让按患者解剖结构定制个体化骨植入物成为可能，这应该会让植入物使用更久。
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AI could also help create bone implants that can be put in place through a small opening to reduce the need for surgery.
AI 还可以帮助制造骨植入物，使其能够通过一个小开口放置到位，从而减少对外科手术的需求。
They could be designed to be compact during insertion then expand inside the body so that it fills the defect once it is in place.
它们可以被设计成在植入时保持紧凑，随后在体内展开，这样一旦就位就能填补缺损部位。
Kumar and his team recently revealed an AI-designed metamaterial that can expand in all directions at once and even be programmed to change shape in specific ways in response to an electrical current.
库马尔和他的团队最近展示了一种由AI设计的超材料，它可以同时向各个方向扩张，甚至还能被编程为响应电流而以特定方式改变形状。
While this one wasn't designed to mimic bone, it has shown what could be possible.
虽然这种材料并不是为模仿骨骼而设计的，但它展示了未来可能实现的方向。
"I think deployable implants are very exciting," says Mirzaali.
“我认为可展开式植入物非常令人兴奋，”米尔扎阿里说。
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