用张量补全加速材料性能预测，误差降10%-20%。
Tensor Completion for Surrogate Modeling of Material Property Prediction
- 将材料设计问题建模为张量补全，利用数据结构减少搜索空间。
- 在多个任务中误差比梯度提升和多层感知机低10%-20%。
- 适合材料研发人员快速筛选高性能组合，尤其适用于高维设计变量。
在设计材料以优化特定性能时，常需探索大量可能的设计组合。例如，元素组成会影响强度或导电性等性质，这些信息对新材料开发至关重要。当设计变量增多时，全面探索所有组合会变得极为耗时。因此，越来越多研究采用机器学习（ML）来预测材料性能。本文将材料性能优化建模为张量补全问题，利用数据集的结构特性，高效处理海量材料配置组合。在多种材料性能预测任务中，实验表明张量补全方法相比梯度提升（GradientBoosting）和多层感知机（MLP）等基线模型，误差降低10%-20%，同时保持相近的训练速度。
原文摘要 · Abstract (English)
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.
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