# ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

arXiv: https://arxiv.org/abs/2211.07881 · HTML: https://mlsanity.com/paper/2211.07881/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 80 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Gaussian Processes for Big Data](https://mlsanity.com/paper/1309.6835/) (2013)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Why do tree-based models still outperform deep learning on tabular data?](https://mlsanity.com/paper/2207.08815/) (2022)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)
- [Characterizing Bias in Classifiers using Generative Models](https://mlsanity.com/paper/1906.11891/) (2019)
- [Autonomous synthesis of metastable materials](https://mlsanity.com/paper/2101.07385/) (2021)
- [Machine-learned metrics for predicting the likelihood of success in materials discovery](https://mlsanity.com/paper/1911.11201/) (2019)
- [Bias and Priors in Machine Learning Calibrations for High Energy Physics](https://mlsanity.com/paper/2205.05084/) (2022)

## Built on by (most cited first, 6 of 6)

- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models](https://mlsanity.com/paper/2502.02189/) (2025)
- [Active Learning of Molecular Data for Task-Specific Objectives](https://mlsanity.com/paper/2408.11191/) (2024)
- [Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data](https://mlsanity.com/paper/2412.17283/) (2024)
- [A Critical Examination of Active Learning Workflows in Materials Science](https://mlsanity.com/paper/2601.05946/) (2026)
- [Measuring trainable degrees of freedom in materials graph neural networks: a random-subspace intrinsic dimension analysis](https://mlsanity.com/paper/2609.36084/) (2026)

## Abstract (arXiv)

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.