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Classification-Based Detection and Quantification of Cross-Domain Data Bias in Materials Discovery.

Giovanni Trezza¹, Eliodoro Chiavazzo¹

¹Department of Energy, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino 10129, Italy.

Journal of Chemical Information and Modeling

|December 16, 2024

Summary

This study introduces a novel method to detect and quantify data bias in artificial intelligence (AI) models, improving predictions for materials discovery by excluding unreliable out-of-the-box samples.

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Area of Science:

Materials Science
Data Science
Artificial Intelligence

Background:

Accurate artificial intelligence (AI)-driven models rely heavily on data quantity and quality.
Bias in sample selection during database generation can lead to unreliable predictions when AI models are applied to data with different origins.
This bias can hinder AI-based materials discovery, even with large, high-quality datasets.

Purpose of the Study:

To develop a method for detecting and quantifying data bias in AI models.
To reduce the impact of data bias on materials discovery.
To enhance the reliability of AI models for diverse and unseen materials.

Main Methods:

A classification strategy is employed to identify and quantify data bias.
The method identifies 'out-of-the-box' materials where pretrained model predictions are likely unreliable.
The approach is validated using superconductor and thermoelectric materials.

Main Results:

The proposed method effectively detects and quantifies data bias.
It successfully identifies materials outside the training data distribution for which predictions are unreliable.
The methodology enhances the reliability of AI models for materials discovery.

Conclusions:

The developed method offers a simple, flexible, and adaptable solution for bias detection in AI.
It improves the trustworthiness of AI models applied to new materials, advancing AI-driven materials discovery.
This approach is compatible with various AI architectures, including graph equivariant neural networks.