# A Critical Examination of Active Learning Workflows in Materials Science

arXiv: https://arxiv.org/abs/2601.05946 · HTML: https://mlsanity.com/paper/2601.05946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 128 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 28 of 28)

- [Learning Active Learning from Data](https://mlsanity.com/paper/1703.03365/) (2017)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [On Statistical Bias In Active Learning: How and When To Fix It](https://mlsanity.com/paper/2101.11665/) (2021)
- [Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification](https://mlsanity.com/paper/2109.10254/) (2021)
- [Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains](https://mlsanity.com/paper/2106.01309/) (2021)
- [Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation](https://mlsanity.com/paper/1810.11890/) (2018)
- [Olympus: a benchmarking framework for noisy optimization and experiment planning](https://mlsanity.com/paper/2010.04153/) (2020)
- [In Defense of Core-set: A Density-aware Core-set Selection for Active Learning](https://mlsanity.com/paper/2206.04838/) (2022)
- [Exploring Representativeness and Informativeness for Active Learning](https://mlsanity.com/paper/1904.06685/) (2019)
- [Data-driven discovery of novel 2D materials by deep generative models](https://mlsanity.com/paper/2206.12159/) (2022)
- [Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study](https://mlsanity.com/paper/2401.08032/) (2024)
- [Materials Property Prediction with Uncertainty Quantification: A Benchmark Study](https://mlsanity.com/paper/2211.02235/) (2022)
- [Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity](https://mlsanity.com/paper/2309.06240/) (2023)
- [Benchmarking Active Learning Strategies for Materials Optimization and Discovery](https://mlsanity.com/paper/2204.05838/) (2022)
- [Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory](https://mlsanity.com/paper/2404.12367/) (2024)
- [Clarifying Trust of Materials Property Predictions using Neural Networks with Distribution-Specific Uncertainty Quantification](https://mlsanity.com/paper/2302.02595/) (2023)
- [Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research](https://mlsanity.com/paper/2410.00544/) (2024)
- [Machine-learned metrics for predicting the likelihood of success in materials discovery](https://mlsanity.com/paper/1911.11201/) (2019)
- [PI is back! Switching Acquisition Functions in Bayesian Optimization](https://mlsanity.com/paper/2211.01455/) (2022)
- [ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data](https://mlsanity.com/paper/2211.07881/) (2022)
- [A General Approach for Determining Applicability Domain of Machine Learning Models](https://mlsanity.com/paper/2406.05143/) (2024)
- [Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not](https://mlsanity.com/paper/2112.06926/) (2021)
- [A Data Driven Sequential Learning Framework to Accelerate and Optimize Multi-Objective Manufacturing Decisions](https://mlsanity.com/paper/2304.09278/) (2023)
- [Overcoming Overconfidence for Active Learning](https://mlsanity.com/paper/2308.10571/) (2023)
- [Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances](https://mlsanity.com/paper/2407.10844/) (2024)
- [Batched Bayesian optimization by maximizing the probability of including the optimum](https://mlsanity.com/paper/2410.06333/) (2024)
- [Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning](https://mlsanity.com/paper/2409.11808/) (2024)
- [Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization](https://mlsanity.com/paper/2509.25538/) (2025)

## Abstract (arXiv)

Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations and the operation of self-driving laboratories. Despite its widespread use, the reliability and effectiveness of AL workflows depend on implicit design assumptions that are rarely examined systematically. Here, we critically assess AL workflows deployed in materials science and investigate how key design choices, such as surrogate models, sampling strategies, uncertainty quantification and evaluation metrics, relate to their performance. By identifying common pitfalls and discussing practical mitigation strategies, we provide guidance to practitioners for the efficient design, assessment, and interpretation of AL workflows in materials science.