Out-of-distribution materials property prediction using adversarial learning based fine-tuning

TL;DR

This paper introduces the Crystal Adversarial Learning (CAL) algorithm, which improves out-of-distribution materials property prediction by generating synthetic data and adversarial fine-tuning, enhancing model robustness with limited samples.

Contribution

The paper presents a novel CAL algorithm and an adversarial fine-tuning approach specifically designed for out-of-distribution materials property prediction, addressing generalization challenges.

Findings

CAL achieves high effectiveness with limited training samples.

Adversarial fine-tuning improves model adaptation to OOD datasets.

The methods enhance robustness and reliability in materials property prediction.

Abstract

The accurate prediction of material properties is crucial in a wide range of scientific and engineering disciplines. Machine learning (ML) has advanced the state of the art in this field, enabling scientists to discover novel materials and design materials with specific desired properties. However, one major challenge that persists in material property prediction is the generalization of models to out-of-distribution (OOD) samples,i.e., samples that differ significantly from those encountered during training. In this paper, we explore the application of advancements in OOD learning approaches to enhance the robustness and reliability of material property prediction models. We propose and apply the Crystal Adversarial Learning (CAL) algorithm for OOD materials property prediction,which generates synthetic data during training to bias the training towards those samples with high prediction uncertainty. We further propose an adversarial learning based targeting finetuning approach to make the model adapted to a particular OOD dataset, as an alternative to traditional fine-tuning. Our experiments demonstrate the success of our CAL algorithm with its high effectiveness in ML with limited samples which commonly occurs in materials science. Our work represents a promising direction toward better OOD learning and materials property prediction.