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.

利用 LatticeML 应用通过机器学习加速高温图形化材料的设计和优化，预测热性能材料的有效弹性模量，并使用 Streamlit 框架创建交互式网页界面

LatticeML：一种预测基于高温图形化材料的有效杨氏模量的数据驱动应用

LatticeML: A data-driven application for predicting the effective Young  Modulus of high temperature graph based architected materials

Machine learning (ML) is emerging as a transformative tool for the design of
architected materials, offering properties that far surpass those achievable
through lab-based trial-and-error methods. However, a major challenge in
current inverse design strategies is their reliance on extensive computational
and/or experimental datasets, which becomes particularly problematic for
designing micro-scale stochastic architected materials that exhibit nonlinear
mechanical behaviors. Here, we introduce a new end-to-end scientific ML
framework, leveraging deep neural operators (DeepONet), to directly learn the
relationship between the complete microstructure and mechanical response of
architected metamaterials from sparse but high-quality in situ experimental
data. The approach facilitates the inverse design of structures tailored to
specific nonlinear mechanical behaviors. Results obtained from spinodal
microstructures, printed using two-photon lithography, reveal that the
prediction error for mechanical responses is within a range of 5 - 10%. Our
work underscores that by employing neural operators with advanced
micro-mechanics experimental techniques, the design of complex
micro-architected materials with desired properties becomes feasible, even in
scenarios constrained by data scarcity. Our work marks a significant
advancement in the field of materials-by-design, potentially heralding a new
era in the discovery and development of next-generation metamaterials with
unparalleled mechanical characteristics derived directly from experimental
insights.

通过深度神经网络算子（DeepONet）直接学习基于稀疏但高质量位于实验数据的超材料完整微结构与其力学响应之间的关系，进而反向设计具有特定非线性力学特性的结构，该方法为设计复杂微结构材料的特性提供了可行性，即使在数据稀缺的情况下。