Architected materials with their unique topology and geometry offer the potential to modify physical and mechanical properties. Machine learning can accelerate the design and optimization of these materials by identifying optimal designs and forecasting performance. This work presents LatticeML, a data-driven application for predicting the effective Young's Modulus of high-temperature graph-based architected materials. The study considers eleven graph-based lattice structures with two high-temperature alloys, Ti-6Al-4V and Inconel 625. Finite element simulations were used to compute the effective Young's Modulus of the 2x2x2 unit cell configurations. A machine learning framework was developed to predict Young's Modulus, involving data collection, preprocessing, implementation of regression models, and deployment of the best-performing model. Five supervised learning algorithms were evaluated, with the XGBoost Regressor achieving the highest accuracy (MSE = 2.7993, MAE = 1.1521, R-squared = 0.9875). The application uses the Streamlit framework to create an interactive web interface, allowing users to input material and geometric parameters and obtain predicted Young's Modulus values.

翻译：具有独特拓扑和几何结构的结构材料，为调控物理和力学性能提供了可能。机器学习可通过识别最优设计并预测性能，加速此类材料的设计与优化。本文提出LatticeML——一种数据驱动的高温石墨基结构材料有效杨氏模量预测应用。研究考虑了两种高温合金（Ti-6Al-4V和Inconel 625）的十一种石墨基晶格结构。通过有限元仿真计算了2x2x2单胞构型的有效杨氏模量，并开发了机器学习框架进行杨氏模量预测，涵盖数据采集、预处理、回归模型实现及最优模型部署。评估了五种监督学习算法，其中XGBoost回归器取得了最高精度（MSE=2.7993，MAE=1.1521，R平方=0.9875）。该应用基于Streamlit框架构建交互式网页界面，允许用户输入材料与几何参数，获取预测的杨氏模量值。