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：结构材料因其独特的拓扑构型和几何形状，具备调控物理与力学性能的潜力。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进程。本文提出LatticeML——一种面向高温图基结构材料有效杨氏模量预测的数据驱动应用。研究选取11种图基晶格结构，采用两种高温合金（Ti-6Al-4V与Inconel 625），通过有限元模拟计算2×2×2单胞构型的有效杨氏模量。开发了包含数据采集、预处理、回归模型实现及最优模型部署的机器学习框架，对五种监督学习算法进行评估，其中XGBoost回归器取得最高精度（MSE=2.7993, MAE=1.1521, R平方=0.9875）。该应用基于Streamlit框架构建交互式网络界面，用户可通过输入材料与几何参数获取杨氏模量预测值。