Tensor Completion for Surrogate Modeling of Material Property Prediction

TL;DR

This paper introduces a tensor completion approach to efficiently predict material properties across vast design spaces, outperforming traditional ML models in accuracy while maintaining similar training speeds.

Contribution

It models material property prediction as a tensor completion problem, leveraging dataset structure to improve accuracy over baseline machine learning methods.

Findings

Tensor completion reduces prediction error by 10-20% compared to baselines.

The method maintains similar training speed to traditional ML models.

Applicable across various material property prediction tasks.

Abstract

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.