# Benchmarking Active Learning Strategies for Materials Optimization and Discovery

arXiv: https://arxiv.org/abs/2204.05838 · HTML: https://mlsanity.com/paper/2204.05838/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [High Dimensional Bayesian Optimisation and Bandits via Additive Models](https://mlsanity.com/paper/1503.01673/) (2015)
- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains](https://mlsanity.com/paper/2106.01309/) (2021)
- [Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping](https://mlsanity.com/paper/2111.07478/) (2021)

## Built on by (most cited first, 11 of 11)

- [Active Learning for Neural PDE Solvers](https://mlsanity.com/paper/2408.01536/) (2024)
- [MADE: Benchmark Environments for Closed-Loop Materials Discovery](https://mlsanity.com/paper/2601.20996/) (2026)
- [Training-Free Active Learning Framework in Materials Science with Large Language Models](https://mlsanity.com/paper/2511.19730/) (2025)
- [When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem](https://mlsanity.com/paper/2511.17760/) (2025)
- [Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing](https://mlsanity.com/paper/2402.19462/) (2024)
- [Active Learning of Molecular Data for Task-Specific Objectives](https://mlsanity.com/paper/2408.11191/) (2024)
- [A Critical Examination of Active Learning Workflows in Materials Science](https://mlsanity.com/paper/2601.05946/) (2026)
- [LLMs as Acquisition Policies for Finite-Pool Materials Optimization: A Controlled Study](https://mlsanity.com/paper/2608.19790/) (2026)
- [Online Surrogate Repair: Decoupling High-Fidelity Feedback from Search Length in Closed-Loop Discovery](https://mlsanity.com/paper/2609.07655/) (2026)
- [Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery](https://mlsanity.com/paper/2610.04134/) (2026)
- [Autonomous Science Bench: Evaluating AI Agents for Autonomous Scientific Discovery](https://mlsanity.com/paper/2610.18449/) (2026)

## Abstract (arXiv)

Autonomous physical science is revolutionizing materials science. In these systems, machine learning controls experiment design, execution, and analysis in a closed loop. Active learning, the machine learning field of optimal experiment design, selects each subsequent experiment to maximize knowledge toward the user goal. Autonomous system performance can be further improved with implementation of scientific machine learning, also known as inductive bias-engineered artificial intelligence, which folds prior knowledge of physical laws (e.g., Gibbs phase rule) into the algorithm. As the number, diversity, and uses for active learning strategies grow, there is an associated growing necessity for real-world reference datasets to benchmark strategies. We present a reference dataset and demonstrate its use to benchmark active learning strategies in the form of various acquisition functions. Active learning strategies are used to rapidly identify materials with optimal physical properties within a ternary materials system. The data is from an actual Fe-Co-Ni thin-film library and includes previously acquired experimental data for materials compositions, X-ray diffraction patterns, and two functional properties of magnetic coercivity and the Kerr rotation. Popular active learning methods along with a recent scientific active learning method are benchmarked for their materials optimization performance. We discuss the relationship between algorithm performance, materials search space complexity, and the incorporation of prior knowledge.