论文标题
AI加速材料信息学方法用于发现延性合金
AI-accelerated Materials Informatics Method for the Discovery of Ductile Alloys
论文作者
论文摘要
在计算材料科学中，一种合金的宏观宏观（例如机械）特性的常见手段是使用依赖某些材料特性（弹性常数，错误拟合量等）的描述符组合来定义模型。通常使用特殊的准随机结构（SQS）计算材料特性，并与密度功能理论（DFT）同时计算。但是，DFT与原子数量进行了立方体尺度，因此对于在许多合金组成上进行筛选是不切实际的。 在这里，我们提出了一种新的方法，该方法结合了建模方法和机器学习的原子势。机器学习的原子间电位比DFT快的数量级，同时达到了相似的精度，可以在整个合金空间上进行可预测性且可进行的高通量筛选。通过预测中透射合金MO-NB-TA的室温延展性来说明所提出的方法。
In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants, misfit volumes, etc.), representative for the macroscopic behavior. The material properties are usually computed using special quasi-random structures (SQSs), in tandem with density functional theory (DFT). However, DFT scales cubically with the number of atoms and is thus impractical for a screening over many alloy compositions. Here, we present a novel methodology which combines modeling approaches and machine-learning interatomic potentials. Machine-learning interatomic potentials are orders of magnitude faster than DFT, while achieving similar accuracy, allowing for a predictive and tractable high-throughput screening over the whole alloy space. The proposed methodology is illustrated by predicting the room temperature ductility of the medium-entropy alloy Mo-Nb-Ta.
