从机器学习原子间势到微观结构：一种通过CALPHAD模型预测的解析导数在高维成分空间中设计旋节线合金的高通量计算框架
From MLIPs to Microstructure: A High-Throughput Computational Framework to Design Spinodal Alloys in High-Dimensional Composition Spaces via Analytic Derivatives of CALPHAD Model Predictions
AI总结：
该研究提出基于CALPHAD的开源工作流程，通过机器学习原子间势训练进行高通量微观结构稳定性分析与可视化，利用解析导数提高计算效率和准确性，以Hf-Nb-Ti-V四元系为例演示了此流程。
AI中文摘要：
识别高维成分空间中发生旋节分解的设计空间区域是合金设计的关键部分。当设计者试图利用旋节线微观结构来调整合金性能时，还需要预测微观结构演变和形态。本文提出了一种基于CALPHAD的开源工作流程，通过机器学习原子间势训练，用于高通量微观结构稳定性分析和可视化。通过高通量机器学习原子间势弹性常数计算来捕获相干应变贡献。将机器学习原子间势生成的热力学模型输入弹性化学相场模拟来预测感兴趣成分的微观结构形态。稳定性分析和相场模拟都利用解析得到的吉布斯自由能海森矩阵来提高计算效率和准确性。我们通过研究Hf-Nb-Ti-V四元系中的微观结构稳定性来演示此工作流程。
英文摘要：
Identifying regions of design space subject to spinodal decomposition is a critical component of alloy design in high-dimensional composition spaces. In cases where designers are seeking to exploit spinodal microstructures to tailor alloy properties, prediction of microstructure evolution and morphology is also needed. In this work, we present a Machine Learning Interatomic Potential (MLIP)-trained, CALPHAD-based, open-source workflow for high-throughput microstructure stability analysis and visualization. In this workflow, coherent strain contributions are captured via high-throughput MLIP elastic constant calculations. To predict microstructure morphology for compositions of interest, MLIP-generated thermodynamic models are fed into an elasto-chemical phase field simulation. Both stability analyses and phase-field simulations utilize analytically-derived Gibbs energy Hessians to improve computational efficiency and accuracy over finite difference approximations. We demonstrate this workflow by investigating microstructure stability in the Hf-Nb-Ti-V quaternary system.
