mlsanityData to 7 Oct 2026

Bayesian optimization for materials design

arXiv 1506.01349 ↗First posted 3 Jun 2015stat.ML math.OCcited by 78

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Abstract (arXiv)

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as possible. We focus on the case when materials designs are parameterized by a low-dimensional vector. Bayesian optimization is built on a statistical technique called Gaussian process regression, which allows predicting the performance of a new design based on previously tested designs. After providing a detailed introduction to Gaussian process regression, we introduce two Bayesian optimization methods: expected improvement, for design problems with noise-free evaluations; and the knowledge-gradient method, which generalizes expected improvement and may be used in design problems with noisy evaluations. Both methods are derived using a value-of-information analysis, and enjoy one-step Bayes-optimality.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2015.

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Citing papers per year: 2021: 13; 2022: 11; 2023: 12; 2024: 12; 2025: 18; 2026: 12.

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  1. 2021Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020cited by 146
  2. 2022Recent Advances in Bayesian Optimizationcited by 74
  3. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
  4. 2024A Systematic Survey on Large Language Models for Algorithm Designcited by 65
  5. 2023Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspacescited by 50
  6. 2021Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domainscited by 46
  7. 2021Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spacescited by 45
  8. 2022Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomescited by 41
  9. 2023Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorialcited by 27
  10. 2021Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimizationcited by 22
  11. 2023Self-Correcting Bayesian Optimization through Bayesian Active Learningcited by 22
  12. 2021Bayesian Optimization over Permutation Spacescited by 17
38 more
  1. 2023Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goalcited by 17
  2. 2024Evolve Cost-aware Acquisition Functions Using Large Language Modelscited by 17
  3. 2024Reinforced In-Context Black-Box Optimizationcited by 14
  4. 2021Accounting for Gaussian Process Imprecision in Bayesian Optimizationcited by 13
  5. 2025LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimizationcited by 13
  6. 2021Multi-Objective Constrained Optimization for Energy Applications via Tree Ensemblescited by 12
  7. 2022A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functionscited by 12
  8. 2024Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaborationcited by 12
  9. 2021Online Calibrated and Conformal Prediction Improves Bayesian Optimizationcited by 10
  10. 2023Transfer Learning for Bayesian Optimization on Heterogeneous Search Spacescited by 9
  11. 2023Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blockscited by 8
  12. 2024Explainable Bayesian Optimizationcited by 8
  13. 2022Event-Triggered Time-Varying Bayesian Optimizationcited by 7
  14. 2022Lower Bounds on the Worst-Case Complexity of Efficient Global Optimizationcited by 7
  15. 2022Global Optimization with Parametric Function Approximationcited by 7
  16. 2025Collaborative Bayesian Optimization via Wasserstein Barycenterscited by 7
  17. 2021Spiderweb nanomechanical resonators via Bayesian optimization: inspired by nature and guided by machine learningcited by 6
  18. 2021Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimizationcited by 6
  19. 2024An Augmented Surprise-guided Sequential Learning Framework for Predicting the Melt Pool Geometrycited by 6
  20. 2025NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Informationcited by 6
  21. 2022Bayesian Optimization For Multi-Objective Mixed-Variable Problemscited by 5
  22. 2022Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimizationcited by 5
  23. 2023Joint Composite Latent Space Bayesian Optimizationcited by 5
  24. 2026Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectoriecited by 5
  25. 2021Self-supervised optimization of random material microstructures in the small-data regimecited by 4
  26. 2024Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Designcited by 4
  27. 2025Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?cited by 4
  28. 2022Optimization of the Shape of a Hydrokinetic Turbine's Draft Tube and Hub Assembly Using Design-by-Morphing with Bayesian Optimizationcited by 3
  29. 2023Guiding the Sequential Experiments in Autonomous Experimentation Platforms through EI-based Bayesian Optimization and Bayesian Model Averagingcited by 3
  30. 2023Bayesian Optimisation Against Climate Change: Applications and Benchmarkscited by 3
  31. 2023Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimizationcited by 3
  32. 2024BO4IO: A Bayesian optimization approach to inverse optimization with uncertainty quantificationcited by 3
  33. 2025Turbocharging Gaussian Process Inference with Approximate Sketch-and-Projectcited by 3
  34. 2021A New Knowledge Gradient-based Method for Constrained Bayesian Optimizationcited by 2
  35. 2022Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingcited by 2
  36. 2023MALIBO: Meta-learning for Likelihood-free Bayesian Optimizationcited by 2
  37. 2025Quantum Non-Linear Bandit Optimizationcited by 2
  38. 2025Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selectioncited by 2

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