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Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains

arXiv 2106.01309 ↗First posted 23 May 2021cond-mat.mtrl-sci cs.LG physics.data-ancited by 46

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Abstract (arXiv)

In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. However, very few studies have evaluated the efficiency of BO as a general optimization algorithm across a broad range of experimental materials science domains. In this work, we evaluate the performance of BO algorithms with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems, namely carbon nanotube polymer blends, silver nanoparticles, lead-halide perovskites, as well as additively manufactured polymer structures and shapes. By defining acceleration and enhancement metrics for general materials optimization objectives, we find that for surrogate model selection, Gaussian Process (GP) with anisotropic kernels (automatic relevance detection, ARD) and Random Forests (RF) have comparable performance and both outperform the commonly used GP without ARD. We discuss the implicit distributional assumptions of RF and GP, and the benefits of using GP with anisotropic kernels in detail. We provide practical insights for experimentalists on surrogate model selection of BO during materials optimization campaigns.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 9 of its 64 references that are in the map. First posted on arXiv 23 May 2021.

Builds on

9 of 64 references.

References · 9

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  3. 2013Gaussian Process Kernels for Pattern Discovery and Extrapolationcited by 131
  4. 2018Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysiscited by 110
  5. 2015Bayesian optimization for materials designcited by 78
  6. 2020Accelerating high-throughput virtual screening through molecular pool-based active learningcited by 59
  7. 2017Batched High-dimensional Bayesian Optimization via Structural Kernel Learningcited by 51
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 6; 2024: 3; 2025: 12; 2026: 15.

46 papers.

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  1. 2023Unexpected Improvements to Expected Improvement for Bayesian Optimizationcited by 144
  2. 2023Bayesian Optimization of Catalysis With In-Context Learningcited by 20
  3. 2021Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learningcited by 16
  4. 2023Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Boundscited by 16
  5. 2022Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)cited by 14
  6. 2022Towards out of distribution generalization for problems in mechanicscited by 11
  7. 2021EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functionscited by 9
  8. 2022Benchmarking Active Learning Strategies for Materials Optimization and Discoverycited by 9
  9. 2021Machine Learning with Knowledge Constraints for Process Optimization of Open-Air Perovskite Solar Cell Manufacturingcited by 8
  10. 2022Uncertainty-Aware Mixed-Variable Machine Learning for Materials Designcited by 6
  11. 2021Towards Futuristic Autonomous Experimentation--A Surprise-Reacting Sequential Experiment Policycited by 4
  12. 2024Regret Analysis for Randomized Gaussian Process Upper Confidence Boundcited by 4
34 more
  1. 2023Bayesian Optimisation Against Climate Change: Applications and Benchmarkscited by 3
  2. 2023Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimizationcited by 3
  3. 2025Adaptive AI decision interface for autonomous electronic material discoverycited by 2
  4. 2025Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AIcited by 2
  5. 2025Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scorescited by 2
  6. 2026Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?cited by 2
  7. 2023Long-run Behaviour of Multi-fidelity Bayesian Optimisationcited by 1
  8. 2025Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactionscited by 1
  9. 2025Machine learning for in-situ composition mapping in a self-driving magnetron sputtering systemcited by 1
  10. 2025A Guide to Bayesian Optimization in Bioprocess Engineeringcited by 1
  11. 2025Training-Free Active Learning Framework in Materials Science with Large Language Modelscited by 1
  12. 2026Engineering Regression Without Real-Data Training: Domain Adaptation for Tabular Foundation Models Using Multi-Dataset Embeddingscited by 1
  13. 2026In-Context Black-Box Optimization with Unreliable Feedbackcited by 1
  14. 2026BoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Taskscited by 1
  15. 2026DASH: Decoupled Adaptive Surrogate - Acquisition Harness for Automated Bayesian Optimizationcited by 1
  16. 2021Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Upcited by 0
  17. 2022Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernelcited by 0
  18. 2024Bayesian optimization for stable properties amid processing fluctuations in sputter depositioncited by 0
  19. 2024SCORE: A 1D Reparameterization Technique to Break Bayesian Optimization's Curse of Dimensionalitycited by 0
  20. 2025Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraintscited by 0
  21. 2025Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effectscited by 0
  22. 2025Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Designcited by 0
  23. 2025CatBOX: A Categorical-Continuous Bayesian Optimization with Spectral Mixture Kernels for Accelerated Catalysis Experimentscited by 0
  24. 2025Bayesian Optimization for Function-Valued Responses under Min-Max Criteriacited by 0
  25. 2026A Critical Examination of Active Learning Workflows in Materials Sciencecited by 0
  26. 2026Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoningcited by 0
  27. 2026Tensor Methods: A Unified and Interpretable Approach for Material Designcited by 0
  28. 2026Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discoverycited by 0
  29. 2026An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functionscited by 0
  30. 2026LEAP: A closed-loop framework for perovskite precursor additive discoverycited by 0
  31. 2026Transferring Information Across Interventions in Causal Bayesian Optimizationcited by 0
  32. 2026tidyHEBO: Robust General-Purpose Bayesian Optimization with Model-Consistent Warping and Pareto Searchcited by 0
  33. 2026ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architecturescited by 0
  34. 2026Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discoverycited by 0