从事航空航天,、能源, 和计算领域前沿工作的公司不断寻找新材料来提高性能。但为了了解这些材料’进入火箭或计算机芯片,后其实际表现如何，公司首先必须制造该材料，然后对其进行测试。那的 因为即使是最强大的模拟技术也很难模拟当今大多数的 固体材料中的复杂化学排列。这个问题增加了材料创新的成本和时间。
Companies working at the frontier of aerospace, energy, and computing are constantly looking for new materials to improve performance. But in order to understand how those materials will actually behave once they’re inside rockets or on computer chips, companies first have to make the material and then test it. That的 because even the most powerful simulation techniques struggle to model the complex chemical arrangements in most of today的 solid materials. The problem adds costs and time to materials innovation.
现在，麻省理工学院的一组研究人员已经创建了一种方法来准确模拟金属,的行为，无论其化学排列的复杂性如何。该方法的核心是机器学习模型，可以使材料模拟更快、更准确。研究人员通过构建捕获化学无序材料中原子环境多样性的训练数据集来改进这些模型。
Now a team of MIT researchers has created a way to accurately model the behavior of metals, regardless of the complexity of their chemical arrangement. At the center of the approach are machine-learning models that make simulations of materials faster and more accurate. The researchers improved those models by building training datasets that capture the diversity of atomic environments in chemically disordered materials.
在 Science Advances, 上的一篇新论文中，研究人员表明他们的方法可用于准确预测各种条件下多种金属合金的材料性能。他们还展示了如何使用该方法来开发新材料,，特别是在实验成本高昂的情况下。
In a new paper in Sciences Advances, the researchers showed their approach could be used to accurately predict material properties for a diverse group of metal alloys under a range of conditions. They also showed how the approach could be used to develop new materials, especially in scenarios where experimentation is expensive.
“本文的重点是金属合金,，这是我工作的领域,，但这可以适用于其他类型的材料,，例如半导体,”，高级作者 Rodrigo Freitas, MIT的 TDK 材料科学与工程职业发展教授说道。 “这并不特定于任何一种应用—您可以使用这种方法来创建新的可持续钢,用于航空航天,等的新材料。 的 是什么让这令人兴奋。”
“The focus of the paper is metallic alloys, which is the field I work in, but this could be adapted to other types of materials, like semiconductors,” says senior author Rodrigo Freitas, MIT的 TDK Career Development Professor in Materials Science and Engineering. “This is not specific to any one application — you could use this approach to create new sustainable steels, new materials for aerospace, and more. That的 what makes this exciting.”
与 Freitas 一起撰写该论文的还有第一作者 Killian Sheriff 博士 ’26; 麻省理工学院博士生 Daniel Shaw 和 Yifan Cao; 以及谢菲尔德大学高级讲师 Lewis R. Owen。
Joining Freitas on the paper are first author Killian Sheriff PhD ’26; MIT PhD students Daniel Xiao and Yifan Cao; and University of Sheffield Senior Lecturer Lewis R. Owen.
材料性能主要由其化学元素的内部排列决定。即使两种材料具有相同的化学元素, 混合物，不同的化学排列也会造成脆性材料和变形而不断裂的材料之间的差异。
Material properties are mostly determined by the internal arrangement of their chemical elements. Even if two materials have the same mix of chemical elements, different chemical arrangements can make the difference between a brittle material and one that deforms without breaking.
捕捉这种区别需要逐个原子地模拟材料。为此, 研究人员依赖于描述原子如何相互作用的模型。在过去的二十年里, 机器学习已经成为构建这些模型的最准确的方法。当材料内部的化学排列遵循高度有序的模式, 时，这种模型效果很好，但 与大多数固体材料, 的情况不同，它们的原子化学排列是无序的，并且从一个区域到另一个区域都不同。
Capturing that distinction requires simulating materials atom by atom. To do that, researchers rely on models that describe how atoms interact with each other. Over the last two decades, machine learning has become the most accurate way to build those models. Such models work well when the chemical arrangements inside materials follow highly ordered patterns, but that的 not the case with most solid materials, whose atomic chemical arrangements are disordered and vary from one region to another.
“我们领域真正的挑战是对这些化学无序相进行建模,” Freitas 说。 “化学无序意味着’有各种各样的局部化学环境,，这对于机器学习模型来说很难学习。这是一个问题，因为我们在实践中使用的每种金属在化学上都是无序的。”
“The real challenge in our field is modelling these chemically disordered phases,” Freitas says. “Chemical disorder means there的 a huge variety of local chemical environments, which is hard for the machine-learning model to learn. This is a problem because every single metal we use in practice is chemically disordered.”
问题归结为缺乏逐个原子模拟的代表性训练数据。 The current leading approach for creating such data works by brute force, often requiring more than 100,000 hours of computation to create the training data for a single material.即使这样，当研究人员改变材料的的成分时，,也不能很好地转移。
The problem comes down to a lack of representative training data for those atom-by-atom simulations. The current leading approach for creating such data works by brute force, often requiring more than 100,000 hours of computation to create the training data for a single material. Even then, it does not transfer well when researchers change the material的 composition.
在之前的工作中, Freitas’小组开发了一种通过分析微小原子团的频率和间距来测量固体材料的化学复杂性的方法。在这项研究, 中，研究人员利用该功能构建了更好的训练数据集。他们使用一种称为信息论的数学方法来生成训练数据集，以捕获无序材料内更广泛的局部化学环境。该方法的工作原理是替换样本中的原子，以减少重复，并将模型暴露在化学环境中，否则模型可能会错过。
In previous work, Freitas group had developed a way to measure the chemical complexity of solid materials by analyzing the frequency and spacing of tiny groups of atoms. For this study, the researchers used that capability to build better training datasets. They used a mathematical approach known as information theory to generate training datasets that capture a wider variety of local chemical environments inside disordered materials. The method works by swapping out atoms from samples to reduce repetition and expose the model to chemical environments it might otherwise miss.
“我们不断优化训练集，以便它捕获尽可能多的不同本地环境,” Freitas 说。 “如果同一种环境多次出现,，我们会将冗余示例替换为模型之前’没有见过的示例。这使得训练集信息更丰富，因为每个示例都添加了一些新内容。”
“We kept optimizing the training set so it captured as many different local environments as possible,” Freitas says. “If the same kind of environment showed up many times, we replaced redundant examples with ones the model hadn’t seen before. That makes the training set much more informative because each example adds something new.”
当在研究人员’数据集,上进行训练时，模型比使用随机抽样或其他流行抽样方法训练的模型更准确地预测材料属性。
When trained on the researchers datasets, the models predicted material properties more accurately than models trained using random sampling or another popular sampling method.
“The starting point for all these atom-by-atom simulations is: Are you able to accurately describe the chemical bond between atoms?” Freitas explains. “If not, it can still teach you about materials in general, but it doesn’t tell you what will happen to specific materials in the real world. This approach makes the simulations high fidelity in terms of their chemistry, to better reflect what的 happening to materials.”
研究人员应用他们的技术为一组化学成分不同的金属合金创建机器学习训练数据集。使用一组机器学习模型,，他们表明在其数据集上训练的模型比谷歌和微软等公司创建的更大的模型更准确。
The researchers applied their technique to create machine-learning training datasets for a group of chemically diverse metal alloys. Using a set of machine-learning models, they showed the models trained on their datasets are more accurate than much larger models created by companies like Google and Microsoft.
“我们已经确信无需使用这些昂贵的暴力方法就可以工作,” Freitas 说。 “I 告诉 Killian, ‘这是一篇好论文。但是，如果您能够证明使用这些模型进行的模拟现在可以准确预测有用的材料特性,，那么它就会成为一篇非常好的论文。 Killian 牢记这一点，并尽可能广泛地对此进行测试。” Sheriff 与肖和曹合作，测试不同合金和特性的方法。该团队还利用 Owen的实验数据，将模拟结果与合金中原子排序的实际测量结果进行比较。
“We got to a point where we were convinced it worked without using these expensive brute-force methods,” Freitas says. “I told Killian, ‘This is a good paper. But if you can show that simulations with these models can now accurately predict useful materials properties, then it becomes a very good paper. Killian took that to heart and tested this as widely as he could.”
Sheriff worked with Xiao and Cao to test the approach across different alloys and properties. The team also drew on Owen的 experimental data to compare the simulations against real measurements of atomic ordering in alloys.
该方法通过捕获样本数据中的隐藏模式来工作, in part,。研究人员将论文中的模式描述为 “ 对某些局部化学构型的微妙能量偏差。”
The method works, in part, by capturing hidden patterns in the sample data. The researchers describe the patterns in the paper as “subtle energetic biases toward certain local chemical configurations.”
这些微小的能量差异很重要，因为它们决定了合金,中形成哪些相，这些相如何随温度和成分,变化，以及最终材料将具有哪些特性。作为一项测试，, Daniel Shaw 领导的模拟表明，的 团队的模型可以预测与实验数据密切匹配的相图。相图描绘了哪些相在不同温度和化学成分,下保持稳定，它们是设计和加工合金的核心工具。
Those small energetic differences matter because they determine which phases form in an alloy, how those phases change with temperature and composition, and ultimately which properties the material will have. As one test, Daniel Xiao led simulations showing that the team的 models could predict phase diagrams that closely matched experimental data. Phase diagrams map which phases are stable across different temperatures and chemical compositions, and they are a central tool for designing and processing alloys.
“相图是人们将材料建模与实际加工决策联系起来的主要方式之一,” Freitas 说。 “如果您正在焊接,铸造,或热处理合金,，您需要知道在不同条件下可能形成哪些相。我们的目标是使此类预测足够准确, 且足够易于理解,，从而成为人们设计材料的一部分。”
“Phase diagrams are one of the main ways people connect materials modeling to real processing decisions,” Freitas says. “If you are welding, casting, or heat-treating an alloy, you need to know which phases are likely to form under different conditions. Our goal is to make these kinds of predictions accurate enough, and accessible enough, that they become part of how people design materials.”
研究人员现在正在使用该方法来研究改变合金的成分如何影响机械性能和辐射耐受性,，目的是设计在恶劣环境下保持坚固和耐损伤的材料。他们还致力于使该方法更易于与材料工程师已经依赖的工具和工作流程一起使用。
The researchers are now using the approach to study how changing an alloy的 composition affects mechanical properties and radiation tolerance, with the goal of designing materials that remain strong and damage-tolerant in harsh environments. They are also working to make the method easier to use with the kinds of tools and workflows materials engineers already rely on.
“行业不会’改变他们做事的方式，如果你’正在创建的东西’不适合他们现有的操作程序,”弗雷塔斯说。 “目标是使这些预测在实际做出材料决策的地方发挥作用。” 这项研究得到了美国空军科学研究办公室的支持。
“Industry isn’t going to change the way they do things if what you’re creating doesn’t fit into their existing operating procedures,” Freitas says. “The goal is to make these predictions useful in the places where materials decisions are actually made.”
The research was supported by the U.S. Air Force Office of Scientific Research.