# Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research

arXiv: https://arxiv.org/abs/2410.00544 · HTML: https://mlsanity.com/paper/2410.00544/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning](https://mlsanity.com/paper/1903.04703/) (2019)
- [Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks](https://mlsanity.com/paper/2012.06051/) (2020)
- [A Study of Bayesian Neural Network Surrogates for Bayesian Optimization](https://mlsanity.com/paper/2305.20028/) (2023)
- [Multi-Fidelity Bayesian Optimization via Deep Neural Networks](https://mlsanity.com/paper/2007.03117/) (2020)
- [Multi-Fidelity Bayesian Optimization with Unreliable Information Sources](https://mlsanity.com/paper/2210.13937/) (2022)
- [Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization](https://mlsanity.com/paper/2211.06149/) (2022)
- [Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes](https://mlsanity.com/paper/2008.03226/) (2020)
- [Machine Learning-Assisted Discovery of Flow Reactor Designs](https://mlsanity.com/paper/2308.08841/) (2023)
- [Long-run Behaviour of Multi-fidelity Bayesian Optimisation](https://mlsanity.com/paper/2312.12633/) (2023)
- [Applying Multi-Fidelity Bayesian Optimization in Chemistry: Open Challenges and Major Considerations](https://mlsanity.com/paper/2409.07190/) (2024)

## Built on by (most cited first, 7 of 7)

- [Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores](https://mlsanity.com/paper/2512.01080/) (2025)
- [A Critical Examination of Active Learning Workflows in Materials Science](https://mlsanity.com/paper/2601.05946/) (2026)
- [Accelerated Dopant Screening in Oxide Semiconductors via Multi-Fidelity Contextual Bandits and a Three-Tier DFT Validation Funnel](https://mlsanity.com/paper/2604.10157/) (2026)
- [Compressing the Validation Bottleneck: An Agentic Self-Driving Lab for Scientific Discovery](https://mlsanity.com/paper/2607.04508/) (2026)
- [Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization](https://mlsanity.com/paper/2607.23404/) (2026)
- [Out-Of-The-Loop Multi-Fidelity Bayesian Optimization](https://mlsanity.com/paper/2608.04113/) (2026)
- [Physics-residual machine learning predicts oxygen-evolution catalyst activity beyond the training range from sparse polarization measurements](https://mlsanity.com/paper/2609.23549/) (2026)

## Abstract (arXiv)

Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at increasing cost. Despite its potential use in chemical tasks, there is a lack of systematic evaluation of the many parameters playing a role in MFBO. In this work, we provide guidelines and recommendations to decide when to use MFBO in experimental settings. We investigate MFBO methods applied to molecules and materials problems. First, we test two different families of acquisition functions in two synthetic problems and study the effect of the informativeness and cost of the approximate function. We use our implementation and guidelines to benchmark three real discovery problems and compare them against their single-fidelity counterparts. Our results may help guide future efforts to implement MFBO as a routine tool in the chemical sciences.