Out-of-distribution materials property prediction using adversarial learning based fine-tuning
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,...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
17.08.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
DOI: | 10.48550/arxiv.2408.09297 |