Tensor Completion for Surrogate Modeling of Material Property Prediction
This work addresses the time-consuming exploration of material configurations for designers and engineers, though it appears incremental as it applies an existing method to a new domain.
The paper tackled the problem of predicting material properties by modeling it as a tensor completion problem, achieving 10-20% decreased error compared to baseline ML models like GradientBoosting and MLP while maintaining similar training speed.
When designing materials to optimize certain properties, there are often many possible configurations of designs that need to be explored. For example, the materials' composition of elements will affect properties such as strength or conductivity, which are necessary to know when developing new materials. Exploring all combinations of elements to find optimal materials becomes very time consuming, especially when there are more design variables. For this reason, there is growing interest in using machine learning (ML) to predict a material's properties. In this work, we model the optimization of certain material properties as a tensor completion problem, to leverage the structure of our datasets and navigate the vast number of combinations of material configurations. Across a variety of material property prediction tasks, our experiments show tensor completion methods achieving 10-20% decreased error compared with baseline ML models such as GradientBoosting and Multilayer Perceptron (MLP), while maintaining similar training speed.