# Tensor Completion for Surrogate Modeling of Material Property Prediction

arXiv: https://arxiv.org/abs/2501.18137 · HTML: https://mlsanity.com/paper/2501.18137/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [Automating Data Science Pipelines with Tensor Completion](https://mlsanity.com/paper/2410.06408/) (2024)

## Built on by (most cited first, 2 of 2)

- [Surrogate Modeling for the Design of Optimal Lattice Structures using Tensor Completion](https://mlsanity.com/paper/2510.07474/) (2025)
- [Tensor Methods: A Unified and Interpretable Approach for Material Design](https://mlsanity.com/paper/2602.10392/) (2026)

## Abstract (arXiv)

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.