Artificial Intelligence for Intermetallic Materials

材料科学通过提供高度优化的结构和功能材料，为应对社会挑战，特别是气候变化做出了贡献。相关材料类别包括用于提高航空、汽车和发电效率的高温合金和轻钢等结构材料，以及分别用于发电和储存的太阳能电池和电池材料等功能材料。材料设计和优化的关键挑战是预测化合物的热力学稳定性，即仅根据化合物的化学组成预测能量稳定的晶体结构。这一预测所涉及的巨大数值计算工作对材料的计算探索设定了一个基本的限制。虽然密度泛函理论计算(DFT)给材料科学带来了革命性的变化，并提供了高度可靠的热力学稳定性预测，但要探索许多技术相关材料所需的化学和结构复杂性，计算成本仍然太高。随着数据驱动的科学发现的到来，我们正在见证科学向第四范式的转变。人工智能(AI)利用数据的巨大潜力将改变我们社会的许多领域，从健康到环境和技术。现在，材料科学已经成为与人工智能发生转变的领域之一。在这个项目中，我们应用现代人工智能技术来预测在高温合金和轻钢中起核心作用的金属间化合物相的热力学稳定性。作为一种新颖和独特的方法，我们将开发和应用一个具有更广泛适用性的混合人工智能生态系统，其中的描述符将被设计为包括(I)原子半径和价电子数等物理性质，(Ii)局部几何信息，(Iii)物理模型中原子间相互作用的定性领域知识，(Iv)DFT的化学键形成的定量领域知识，以及(V)结构-能量关系的定量领域知识。这些描述符将与不同的回归模型结合使用，并结合降维、超优化和重要性分析。这使我们能够构建混合人工智能模型，该模型足够健壮，可以探索化学和结构上复杂的拓扑紧密堆积相，否则无法访问这些相。杂化人工智能对这些金属间化合物材料的结构稳定性和亚晶格占有率的预测将通过本项目的实验测量得到证实。我们预计，这种材料的混合人工智能将为法国和德国的材料科学开创新的有前途的研究方向。

Materials science contributes to facing the challenges of our society and particularly the climate change by delivering highly optimized structural and functional materials. The relevant material classes range from structural materials like superalloys and light-weight steels for increased efficiency in aviation, automobiles and power generation to functional materials like solar cell and battery materials for electric-power generation and storage respectively. The key challenge for materials design and optimization is the prediction of the thermodynamic stability of a compound, i.e. the prediction of the energetically stable crystal structure from only its chemical composition. The enormous numerical effort involved in this prediction sets a fundamental limit on the computational exploration of materials. While density-functional theory calculation (DFT) revolutionized materials science and provides highly reliable predictions of thermodynamic stability, it is still too computationally expensive to explore the chemical and structural complexity that is needed for many technologically relevant materials. With the advent of data-driven scientific discovery, we are witnessing the change to the fourth paradigm in science. The enormous potential of data exploitation with artificial intelligence (AI) will change many fields of our society ranging from health to environment and technology. One of the fields that are changing gears with AI already now is materials science. In this project, we apply modern AI techniques to the prediction of the thermodynamic stability of intermetallic phases that play a central role in superalloys and light-weight steels. As a novel and unique approach, we will develop and apply a hybrid-AI ecosystem with broader applicability, in which descriptors will be designed to include (i) physical properties like atomic radius and valence electrons number, (ii) local geometric information, (iii) qualitative domain knowledge of interatomic interactions in terms of physical models, (iv) quantitative domain-knowledge of chemical bond-formation from DFT, and (v) quantitative domain knowledge on the structure-energy relation. These descriptors will be used with different regression models in combination with dimensionality reduction, hyper-optimization and importance analysis. This enables us to construct hybrid-AI models that are sufficiently robust to explore chemically and structurally complex topologically close-packed phases that are not accessible otherwise. The predictions of the hybrid-AI on structural stability and sublattice occupancy of these intermetallic materials will be confirmed by experimental measurements within this project. We expect that this hybrid-AI for materials will initiate new promising directions of research for the benefit of materials science in France and Germany.