  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.
